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ORCIDTaghi M. Khoshgoftaar
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- affiliation: Florida Atlantic University, Boca Raton, Florida, USA
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2020 – today
- 2024
[j211]Preston Billion Polak, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Low-shot learning and class imbalance: a survey. J. Big Data 11(1): 1 (2024)- [j210]John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang:
Data reduction techniques for highly imbalanced medicare Big Data. J. Big Data 11(1): 8 (2024) - [j209]Safak Kayikci, Taghi M. Khoshgoftaar:
Blockchain meets machine learning: a survey. J. Big Data 11(1): 9 (2024) - [j208]Robert K. L. Kennedy, Flavio Villanustre
, Taghi M. Khoshgoftaar, Zahra Salekshahrezaee:
Synthesizing class labels for highly imbalanced credit card fraud detection data. J. Big Data 11(1): 38 (2024) - [j207]Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods. J. Big Data 11(1): 44 (2024) - [j206]Michael Lowe, Joseph D. Prusa, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
Advancing machine learning with OCR2SEQ: an innovative approach to multi-modal data augmentation. J. Big Data 11(1): 86 (2024) - [c402]Joffrey L. Leevy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
A Review of Unsupervised Anomaly Detection Techniques for Health Insurance Fraud. BigDataService 2024: 141-149 - 2023
- [j205]Debarshi Datta, Safiya George Dalmida, Laurie Martinez, David Newman, Javad Hashemi, Taghi M. Khoshgoftaar, Connor Shorten, Candice Sareli, Paula Eckardt:
Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida. Frontiers Digit. Health 5 (2023) - [j204]Justin M. Johnson, Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
Learning from Highly Imbalanced Big Data with Label Noise. Int. J. Artif. Intell. Tools 32(5): 2360003:1-2360003:20 (2023) - [j203]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
The effect of feature extraction and data sampling on credit card fraud detection. J. Big Data 10(1): 6 (2023) - [j202]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
An approach to application-layer DoS detection. J. Big Data 10(1): 22 (2023) - [j201]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Evaluating classifier performance with highly imbalanced Big Data. J. Big Data 10(1): 42 (2023) - [j200]Joffrey L. Leevy, Justin M. Johnson, John T. Hancock, Taghi M. Khoshgoftaar:
Threshold optimization and random undersampling for imbalanced credit card data. J. Big Data 10(1): 58 (2023) - [j199]Safak Kayikci, Taghi M. Khoshgoftaar:
Breast cancer prediction using gated attentive multimodal deep learning. J. Big Data 10(1): 62 (2023) - [j198]Robert K. L. Kennedy, Zahra Salekshahrezaee, Flavio Villanustre, Taghi M. Khoshgoftaar:
Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning. J. Big Data 10(1): 106 (2023) - [j197]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Comparative analysis of binary and one-class classification techniques for credit card fraud data. J. Big Data 10(1): 118 (2023) - [j196]John T. Hancock, Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar:
Explainable machine learning models for Medicare fraud detection. J. Big Data 10(1): 154 (2023) - [j195]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh:
Investigating the effectiveness of one-class and binary classification for fraud detection. J. Big Data 10(1): 157 (2023) - [j194]Justin M. Johnson, Taghi M. Khoshgoftaar:
Data-Centric AI for Healthcare Fraud Detection. SN Comput. Sci. 4(4): 389 (2023) - [j193]John T. Hancock, Taghi M. Khoshgoftaar:
Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data. SN Comput. Sci. 4(5): 462 (2023) - [c401]Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
A Novel Approach to Synthesize Class Labels in Highly Imbalanced Large Data. CogMI 2023: 11-17 - [c400]Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches. CogMI 2023: 75-82 - [c399]John T. Hancock, Taghi M. Khoshgoftaar:
Data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big Data. ICMLA 2023: 465-471 - [c398]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh:
One-Class Classifier Performance: Comparing Majority versus Minority Class Training. ICTAI 2023: 86-91 - [c397]John T. Hancock, Richard A. Bauder, Taghi M. Khoshgoftaar:
A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers. ICTAI 2023: 92-98 - [c396]Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique. IRI 2023: 121-126 - [c395]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud. IRI 2023: 267-272 - [c394]Robert K. L. Kennedy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method. IRI 2023: 303-308 - [c393]Huanjing Wang, John T. Hancock, Taghi M. Khoshgoftaar:
Improving Medicare Fraud Detection through Big Data Size Reduction Techniques. SOSE 2023: 208-217 - 2022
- [j192]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
IoT information theft prediction using ensemble feature selection. J. Big Data 9(1): 6 (2022) - [j191]Rick Sauber-Cole, Taghi M. Khoshgoftaar:
The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J. Big Data 9(1): 98 (2022) - [j190]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
A new feature popularity framework for detecting cyberattacks using popular features. J. Big Data 9(1): 119 (2022) - [j189]Justin M. Johnson, Taghi M. Khoshgoftaar:
A Survey on Classifying Big Data with Label Noise. ACM J. Data Inf. Qual. 14(4): 23:1-23:43 (2022) - [j188]Justin M. Johnson, Taghi M. Khoshgoftaar:
Encoding High-Dimensional Procedure Codes for Healthcare Fraud Detection. SN Comput. Sci. 3(5): 362 (2022) - [j187]John T. Hancock, Taghi M. Khoshgoftaar:
Hyperparameter Tuning for Medicare Fraud Detection in Big Data. SN Comput. Sci. 3(6): 440 (2022) - [c392]Robert K. L. Kennedy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data. CogMI 2022: 52-58 - [c391]John T. Hancock, Justin M. Johnson, Taghi M. Khoshgoftaar:
A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data. CIC 2022: 135-142 - [c390]Erika Cardenas, Connor Shorten, Taghi M. Khoshgoftaar, Borivoje Furht:
A Comparison of House Price Classification with Structured and Unstructured Text Data. FLAIRS 2022 - [c389]Connor Shorten, Taghi M. Khoshgoftaar:
An Exploration of Consistency Learning with Data Augmentation. FLAIRS 2022 - [c388]Connor Shorten, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard:
Predicting the Severity of COVID-19 Respiratory Illness with Deep Learning. FLAIRS 2022 - [c387]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. ICMLA 2022: 1419-1426 - [c386]Justin M. Johnson, Taghi M. Khoshgoftaar:
Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification. ICMLA 2022: 1427-1434 - [c385]Joffrey L. Leevy, Taghi M. Khoshgoftaar, John T. Hancock:
Evaluating Performance Metrics for Credit Card Fraud Classification. ICTAI 2022: 1336-1341 - [c384]Rick Sauber-Cole, Taghi M. Khoshgoftaar, Justin M. Johnson:
GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects. ICTAI 2022: 1419-1424 - [c383]Connor Shorten, Erika Cardenas, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard:
Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification. ICTAI 2022: 1449-1454 - [c382]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Class-Imbalanced Study with Feature Extraction via PCA and Convolutional Autoencoder. IRI 2022: 63-68 - [c381]Justin M. Johnson, Taghi M. Khoshgoftaar:
Healthcare Provider Summary Data for Fraud Classification. IRI 2022: 236-242 - [c380]John T. Hancock, Taghi M. Khoshgoftaar:
Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection. IRI 2022: 243-249 - [c379]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
The Effects of Random Undersampling for Big Data Medicare Fraud Detection. SOSE 2022: 141-146 - 2021
- [j186]Joffrey L. Leevy, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar:
Detecting cybersecurity attacks across different network features and learners. J. Big Data 8(1): 1-29 (2021) - [j185]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A reconstruction error-based framework for label noise detection. J. Big Data 8(1): 1-16 (2021) - [j184]Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht:
Deep Learning applications for COVID-19. J. Big Data 8(1): 1-54 (2021) - [j183]Flavio Villanustre, Arjuna Chala, Roger Dev, Lili Xu, Jesse Shaw, Borko Furht, Taghi M. Khoshgoftaar:
Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm. J. Big Data 8(1): 33 (2021) - [j182]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting web attacks using random undersampling and ensemble learners. J. Big Data 8(1): 75 (2021) - [j181]Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht:
Text Data Augmentation for Deep Learning. J. Big Data 8(1): 101 (2021) - [j180]Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar:
A literature review on one-class classification and its potential applications in big data. J. Big Data 8(1): 122 (2021) - [j179]John T. Hancock, Taghi M. Khoshgoftaar:
Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection. SN Comput. Sci. 2(4): 268 (2021) - [j178]Justin M. Johnson, Taghi M. Khoshgoftaar:
Medical Provider Embeddings for Healthcare Fraud Detection. SN Comput. Sci. 2(4): 276 (2021) - [c378]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Jared M. Peterson:
Mitigating Class Imbalance for IoT Network Intrusion Detection: A Survey. BigDataService 2021: 143-148 - [c377]John T. Hancock, Taghi M. Khoshgoftaar:
Leveraging LightGBM for Categorical Big Data. BigDataService 2021: 149-154 - [c376]Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
An Examination of Neural Networks on Cluster Computers. BigDataService 2021: 155-160 - [c375]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
An Easy-to-Classify Approach for the Bot-IoT Dataset. CogMI 2021: 172-179 - [c374]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Naeem Seliya:
IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection. CIC 2021: 41-49 - [c373]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. CSR 2021: 27-34 - [c372]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers. ICMLA 2021: 30-37 - [c371]Connor Shorten, Taghi M. Khoshgoftaar:
KerasBERT: Modeling the Keras Language. ICMLA 2021: 219-226 - [c370]John T. Hancock, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
Detecting SSH and FTP Brute Force Attacks in Big Data. ICMLA 2021: 760-765 - [c369]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
Detecting Information Theft Attacks in the Bot-IoT Dataset. ICMLA 2021: 807-812 - [c368]Justin M. Johnson, Taghi M. Khoshgoftaar:
Robust Thresholding Strategies for Highly Imbalanced and Noisy Data. ICMLA 2021: 1182-1188 - [c367]Connor Shorten, Taghi M. Khoshgoftaar:
Investigating the Generalization of Image Classifiers with Augmented Test Sets. ICTAI 2021: 10-17 - [c366]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling. ICTAI 2021: 217-223 - [c365]Robert K. L. Kennedy, Justin M. Johnson, Taghi M. Khoshgoftaar:
The Effects of Class Label Noise on Highly-Imbalanced Big Data. ICTAI 2021: 1427-1433 - [c364]Justin M. Johnson, Taghi M. Khoshgoftaar:
Output Thresholding for Ensemble Learners and Imbalanced Big Data. ICTAI 2021: 1449-1454 - [c363]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Detecting Slow Application-Layer DoS Attacks With PCA. IRI 2021: 176-183 - [c362]Michael Crawford, Taghi M. Khoshgoftaar:
Using Inductive Transfer Learning to Improve Hotel Review Spam Detection. IRI 2021: 248-254 - [c361]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting Web Attacks in Severely Imbalanced Network Traffic Data. IRI 2021: 267-273 - [c360]John T. Hancock, Taghi M. Khoshgoftaar:
Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. IRI 2021: 348-354 - [c359]Justin M. Johnson, Taghi M. Khoshgoftaar:
Encoding Techniques for High-Cardinality Features and Ensemble Learners. IRI 2021: 355-361 - [c358]Aline Anacleto, Taghi M. Khoshgoftaar, Evangelos I. Kaisar:
Predicting Traffic Incidents in Road Networks Using Vehicle Detector Data. ITSC 2021: 1431-1436 - [c357]Jared M. Peterson, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Review and Analysis of the Bot-IoT Dataset. SOSE 2021: 20-27 - 2020
- [j177]Richard A. Bauder, Taghi M. Khoshgoftaar:
A study on rare fraud predictions with big Medicare claims fraud data. Intell. Data Anal. 24(1): 141-161 (2020) - [j176]Justin M. Johnson, Taghi M. Khoshgoftaar:
The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data. Inf. Syst. Frontiers 22(5): 1113-1131 (2020) - [j175]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder:
Investigating class rarity in big data. J. Big Data 7(1): 23 (2020) - [j174]John T. Hancock, Taghi M. Khoshgoftaar:
Survey on categorical data for neural networks. J. Big Data 7(1): 28 (2020) - [j173]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
Investigating the relationship between time and predictive model maintenance. J. Big Data 7(1): 36 (2020) - [j172]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Flavio Villanustre:
Survey on RNN and CRF models for de-identification of medical free text. J. Big Data 7(1): 73 (2020) - [j171]John T. Hancock, Taghi M. Khoshgoftaar:
CatBoost for big data: an interdisciplinary review. J. Big Data 7(1): 94 (2020) - [j170]Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data. J. Big Data 7(1): 104 (2020) - [j169]Aaron N. Richter, Taghi M. Khoshgoftaar:
Sample size determination for biomedical big data with limited labels. Netw. Model. Anal. Health Informatics Bioinform. 9(1): 12 (2020) - [c356]Joffrey L. Leevy, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar:
Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners. CogMI 2020: 190-197 - [c355]Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Short Survey of LSTM Models for De-identification of Medical Free Text. CIC 2020: 117-124 - [c354]Justin M. Johnson, Taghi M. Khoshgoftaar:
Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction. CIC 2020: 145-152 - [c353]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Evaluating The Number of Trainable Parameters on Deep Maxout and LReLU Networks for Visual Recognition. ICMLA 2020: 415-421 - [c352]John T. Hancock, Taghi M. Khoshgoftaar:
Performance of CatBoost and XGBoost in Medicare Fraud Detection. ICMLA 2020: 572-579 - [c351]Robert K. L. Kennedy, Taghi M. Khoshgoftaar:
Accelerated Deep Learning on HPCC Systems. ICMLA 2020: 847-852 - [c350]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Detection Methods of Slow Read DoS Using Full Packet Capture Data. IRI 2020: 9-16 - [c349]John T. Hancock, Taghi M. Khoshgoftaar:
Medicare Fraud Detection using CatBoost. IRI 2020: 97-103 - [c348]Justin M. Johnson, Taghi M. Khoshgoftaar:
Semantic Embeddings for Medical Providers and Fraud Detection. IRI 2020: 224-230
2010 – 2019
- 2019
- [j168]Aaron N. Richter, Taghi M. Khoshgoftaar:
Efficient learning from big data for cancer risk modeling: A case study with melanoma. Comput. Biol. Medicine 110: 29-39 (2019) - [j167]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Maxout Networks for Visual Recognition. Int. J. Multim. Data Eng. Manag. 10(4): 1-25 (2019) - [j166]Joseph D. Prusa, Ryan Sagul, Taghi M. Khoshgoftaar:
Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. Inf. Syst. Frontiers 21(1): 109-123 (2019) - [j165]Robert K. L. Kennedy, Taghi M. Khoshgoftaar, Flavio Villanustre, Timothy Humphrey:
A parallel and distributed stochastic gradient descent implementation using commodity clusters. J. Big Data 6: 16 (2019) - [j164]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J. Big Data 6: 21 (2019) - [j163]Justin M. Johnson, Taghi M. Khoshgoftaar:
Survey on deep learning with class imbalance. J. Big Data 6: 27 (2019) - [j162]Connor Shorten, Taghi M. Khoshgoftaar:
A survey on Image Data Augmentation for Deep Learning. J. Big Data 6: 60 (2019) - [j161]Justin M. Johnson, Taghi M. Khoshgoftaar:
Medicare fraud detection using neural networks. J. Big Data 6: 63 (2019) - [j160]Chad Calvert, Taghi M. Khoshgoftaar:
Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data. J. Big Data 6: 67 (2019) - [j159]Victor M. Herrera, Taghi M. Khoshgoftaar, Flavio Villanustre, Borko Furht:
Random forest implementation and optimization for Big Data analytics on LexisNexis's high performance computing cluster platform. J. Big Data 6: 68 (2019) - [j158]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya:
Examining characteristics of predictive models with imbalanced big data. J. Big Data 6: 69 (2019) - [j157]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Evaluation of maxout activations in deep learning across several big data domains. J. Big Data 6: 72 (2019) - [j156]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder:
Severely imbalanced Big Data challenges: investigating data sampling approaches. J. Big Data 6: 107 (2019) - [j155]Aaron N. Richter, Taghi M. Khoshgoftaar:
Melanoma risk modeling from limited positive samples. Netw. Model. Anal. Health Informatics Bioinform. 8(1): 7 (2019) - [c347]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya:
Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data. BigDataService 2019: 346-356 - [c346]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Maxout Neural Network for Big Data Medical Fraud Detection. BigDataService 2019: 357-362 - [c345]Stevens Dormezil, Taghi M. Khoshgoftaar, Federica Robinson-Bryant:
Differentiating between Educational Data Mining and Learning Analytics: A Bibliometric Approach. EDM (Workshops) 2019: 17-22 - [c344]Gabriel Castaneda, Paul Morris, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Investigation of Maxout Activations on Convolutional Neural Networks for Big Data Text Sentiment Analysis. FLAIRS 2019: 250-256 - [c343]Chad Calvert, Clifford Kemp, Taghi M. Khoshgoftaar, Maryam M. Najafabadi:
Detecting Slow HTTP POST DoS Attacks Using Netflow Features. FLAIRS 2019: 387-390 - [c342]Justin M. Johnson, Taghi M. Khoshgoftaar:
Deep Learning and Thresholding with Class-Imbalanced Big Data. ICMLA 2019: 755-762 - [c341]Aaron N. Richter, Taghi M. Khoshgoftaar:
Learning Curve Estimation with Large Imbalanced Datasets. ICMLA 2019: 763-768 - [c340]Huanjing Wang, Taghi M. Khoshgoftaar:
A Study on Software Metric Selection for Software Fault Prediction. ICMLA 2019: 1045-1050 - [c339]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
The Effect of Time on the Maintenance of a Predictive Model. ICMLA 2019: 1891-1896 - [c338]Aaron N. Richter, Taghi M. Khoshgoftaar:
Approximating Learning Curves for Imbalanced Big Data with Limited Labels. ICTAI 2019: 237-242 - [c337]Chad Calvert, Taghi M. Khoshgoftaar:
Threshold Based Optimization of Performance Metrics with Severely Imbalanced Big Security Data. ICTAI 2019: 1328-1334 - [c336]Richard A. Bauder, Matthew Herland, Taghi M. Khoshgoftaar:
Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. IRI 2019: 9-14 - [c335]Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data. IRI 2019: 83-88 - [c334]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Deep Learning with Maxout Activations for Visual Recognition and Verification. IRI 2019: 135-142 - [c333]Justin M. Johnson, Taghi M. Khoshgoftaar:
Deep Learning and Data Sampling with Imbalanced Big Data. IRI 2019: 175-183 - [e4]M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya:
18th IEEE International Conference On Machine Learning And Applications, ICMLA 2019, Boca Raton, FL, USA, December 16-19, 2019. IEEE 2019, ISBN 978-1-7281-4550-1 [contents] - 2018
- [j154]Aaron N. Richter, Taghi M. Khoshgoftaar:
A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif. Intell. Medicine 90: 1-14 (2018) - [j153]Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Inf. Sci. Syst. 6(1): 9 (2018) - [j152]Karl R. Weiss, Taghi M. Khoshgoftaar:
A Study of the Impact of Base Traditional Learners on Transfer Learning Algorithms. Int. J. Artif. Intell. Tools 27(6): 1850022:1-1850022:33 (2018) - [j151]Sahar Sohangir, Dingding Wang, Anna Pomeranets, Taghi M. Khoshgoftaar:
Big Data: Deep Learning for financial sentiment analysis. J. Big Data 5: 3 (2018) - [j150]Matthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder:
Big Data fraud detection using multiple medicare data sources. J. Big Data 5: 29 (2018) - [j149]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
A survey on addressing high-class imbalance in big data. J. Big Data 5: 42 (2018) - [j148]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Social media for polling and predicting United States election outcome. Soc. Netw. Anal. Min. 8(1): 48:1-48:16 (2018) - [c332]Aaron N. Richter, Taghi M. Khoshgoftaar:
Melanoma Risk Prediction with Structured Electronic Health Records. BCB 2018: 194-199 - [c331]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
The Impact of Malicious Accounts on Political Tweet Sentiment. CIC 2018: 197-202 - [c330]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Location-Based Twitter Sentiment Analysis for Predicting the U.S. 2016 Presidential Election. FLAIRS 2018: 265-270 - [c329]Richard A. Bauder, Taghi M. Khoshgoftaar, Amri Napolitano:
Fraud Detection with a Limited Number of Known Fraudulent Medicare Providers. FLAIRS 2018: 299-304 - [c328]Richard A. Bauder, Taghi M. Khoshgoftaar:
The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels. FLAIRS 2018: 404-409 - [c327]Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin:
An Empirical Study on Class Rarity in Big Data. ICMLA 2018: 785-790 - [c326]Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin:
Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection. ICTAI 2018: 137-142 - [c325]Aaron N. Richter, Taghi M. Khoshgoftaar:
Building and Interpreting Risk Models from Imbalanced Clinical Data. ICTAI 2018: 143-150 - [c324]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Survey of Medicare Data Processing and Integration for Fraud Detection. IRI 2018: 9-14 - [c323]Tawfiq Hasanin, Taghi M. Khoshgoftaar:
The Effects of Random Undersampling with Simulated Class Imbalance for Big Data. IRI 2018: 70-79 - [c322]Richard A. Bauder, Taghi M. Khoshgoftaar:
Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data. IRI 2018: 80-87 - [c321]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Utilizing Netflow Data to Detect Slow Read Attacks. IRI 2018: 108-116 - [c320]Richard A. Bauder, Raquel da Rosa, Taghi M. Khoshgoftaar:
Identifying Medicare Provider Fraud with Unsupervised Machine Learning. IRI 2018: 285-292 - [c319]Ahmad Abu Shanab, Taghi M. Khoshgoftaar:
Is Gene Selection Enough for Imbalanced Bioinformatics Data? IRI 2018: 346-355 - [c318]Ahmad Abu Shanab, Taghi M. Khoshgoftaar:
Filter-Based Subset Selection for Easy, Moderate, and Hard Bioinformatics Data. IRI 2018: 372-377 - 2017
- [j147]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Improving deep neural network design with new text data representations. J. Big Data 4: 7 (2017) - [j146]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Flavio Villanustre, John Holt:
Large-scale distributed L-BFGS. J. Big Data 4: 22 (2017) - [j145]Oscar Day, Taghi M. Khoshgoftaar:
A survey on heterogeneous transfer learning. J. Big Data 4: 29 (2017) - [j144]Kewen Li, Lu Liu, Jiannan Zhai, Taghi M. Khoshgoftaar, Mingwen Shao, Wenying Liu:
Reliability Evaluation Model of Component-Based Software Based on Complex Network Theory. Qual. Reliab. Eng. Int. 33(3): 543-550 (2017) - [j143]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
Improving detection of untrustworthy online reviews using ensemble learners combined with feature selection. Soc. Netw. Anal. Min. 7(1): 37:1-37:18 (2017) - [c317]Aaron N. Richter, Taghi M. Khoshgoftaar:
Predicting sentinel node status in melanoma from a real-world EHR dataset. BIBM 2017: 1872-1878 - [c316]Gabriel Castaneda, Taghi M. Khoshgoftaar:
A Review of Performance Evaluation on 2D Face Databases. BigDataService 2017: 218-223 - [c315]Karl R. Weiss, Taghi M. Khoshgoftaar:
Detection of Phishing Webpages Using Heterogeneous Transfer Learning. CIC 2017: 190-197 - [c314]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Exploring the Effectiveness of Twitter at Polling the United States 2016 Presidential Election. CIC 2017: 283-290 - [c313]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp:
A Text Mining Approach for Anomaly Detection in Application Layer DDoS Attacks. FLAIRS 2017: 312-317 - [c312]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Deep Neural Network Architecture for Character-Level Learning on Short Text. FLAIRS 2017: 353-358 - [c311]Richard A. Bauder, Taghi M. Khoshgoftaar:
Multivariate Anomaly Detection in Medicare using Model Residuals and Probabilistic Programming. FLAIRS 2017: 417-422 - [c310]Karl R. Weiss, Taghi M. Khoshgoftaar:
Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance. ICMLA 2017: 337-343 - [c309]Richard A. Bauder, Taghi M. Khoshgoftaar:
Medicare Fraud Detection Using Machine Learning Methods. ICMLA 2017: 858-865 - [c308]Karl R. Weiss, Taghi M. Khoshgoftaar:
Evaluation of Transfer Learning Algorithms Using Different Base Learners. ICTAI 2017: 187-196 - [c307]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Training Convolutional Networks on Truncated Text. ICTAI 2017: 330-335 - [c306]Joseph D. Prusa, Ryan Sagul, Taghi M. Khoshgoftaar, Michael Sterling:
Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. IRI 2017: 42-48 - [c305]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp:
User Behavior Anomaly Detection for Application Layer DDoS Attacks. IRI 2017: 154-161 - [c304]Karl R. Weiss, Taghi M. Khoshgoftaar:
Analysis of Transfer Learning Performance Measures. IRI 2017: 338-345 - [c303]Sara Landset, Michael F. Bergeron, Taghi M. Khoshgoftaar:
Using Weather and Playing Surface to Predict the Occurrence of Injury in Major League Soccer Games: A Case Study. IRI 2017: 366-371 - [c302]Aaron N. Richter, Taghi M. Khoshgoftaar:
Modernizing Analytics for Melanoma with a Large-Scale Research Dataset. IRI 2017: 551-558 - [c301]Richard A. Bauder, Taghi M. Khoshgoftaar:
Estimating Outlier Score Probabilities. IRI 2017: 559-568 - [c300]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims. IRI 2017: 579-588 - 2016
- [j142]Kewen Li, Lu Liu, Jiannan Zhai, Taghi M. Khoshgoftaar, Timing Li:
The improved grey model based on particle swarm optimization algorithm for time series prediction. Eng. Appl. Artif. Intell. 55: 285-291 (2016) - [j141]Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang:
A survey of transfer learning. J. Big Data 3: 9 (2016) - [c299]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
Integrating Multiple Data Sources to Enhance Sentiment Prediction. CIC 2016: 285-291 - [c298]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Amri Napolitano, Charles Wheelus:
RUDY Attack: Detection at the Network Level and Its Important Features. FLAIRS 2016: 288-293 - [c297]Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa:
Reducing Feature Set Explosion to Facilitate Real-World Review Spam Detection. FLAIRS 2016: 304-309 - [c296]Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data. FLAIRS 2016: 322-328 - [c295]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Comparing Approaches for Combining Data Sampling and Feature Selection to Address Key Data Quality Issues in Tweet Sentiment Analysis. FLAIRS 2016: 608-613 - [c294]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Necessity of Feature Selection when Augmenting Tweet Sentiment Feature Spaces with Emoticons. FLAIRS 2016: 614-620 - [c293]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
An Investigation of Ensemble Techniques for Detection of Spam Reviews. ICMLA 2016: 127-133 - [c292]Karl R. Weiss, Taghi M. Khoshgoftaar:
Investigating Transfer Learners for Robustness to Domain Class Imbalance. ICMLA 2016: 207-213 - [c291]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims. ICMLA 2016: 347-354 - [c290]Karl R. Weiss, Taghi M. Khoshgoftaar:
An Investigation of Transfer Learning and Traditional Machine Learning Algorithms. ICTAI 2016: 283-290 - [c289]Richard A. Bauder, Taghi M. Khoshgoftaar, Aaron N. Richter, Matthew Herland:
Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims. ICTAI 2016: 784-790 - [c288]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper). IRI 2016: 11-19 - [c287]Karl R. Weiss, Taghi M. Khoshgoftaar, Oneeb Rehman:
Designing a Testing Framework for Transfer Learning Algorithms (Application Paper). IRI 2016: 152-159 - [c286]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
Cross-Domain Sentiment Analysis: An Empirical Investigation. IRI 2016: 160-165 - [c285]Aaron N. Richter, Taghi M. Khoshgoftaar:
Predicting Cancer Relapse with Clinical Data: A Survey of Current Techniques. IRI 2016: 369-376 - [c284]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Investigating the Variation of Ensemble Size on Bagging-Based Classifier Performance in Imbalanced Bioinformatics Datasets. IRI 2016: 377-383 - [c283]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Designing a Better Data Representation for Deep Neural Networks and Text Classification. IRI 2016: 411-416 - [p5]Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang:
Transfer Learning Techniques. Big Data Technologies and Applications 2016: 53-99 - [p4]Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic:
Deep Learning Techniques in Big Data Analytics. Big Data Technologies and Applications 2016: 133-156 - [p3]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Is Data Sampling Required When Using Random Forest for Classification on Imbalanced Bioinformatics Data? Theoretical Information Reuse and Integration 2016: 157-171 - 2015
- [j140]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Investigation on Wrapper-Based Feature Selection for Predicting Software Quality. Int. J. Softw. Eng. Knowl. Eng. 25(1): 93-114 (2015) - [j139]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Investigating Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. 25(1): 115-146 (2015) - [j138]Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya:
On the Stability of Feature Selection Methods in Software Quality Prediction: An Empirical Investigation. Int. J. Softw. Eng. Knowl. Eng. 25(9-10): 1467-1490 (2015) - [j137]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Aggregating Data Sampling with Feature Subset Selection to Address Skewed Software Defect Data. Int. J. Softw. Eng. Knowl. Eng. 25(9-10): 1531-1550 (2015) - [j136]Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic:
Deep learning applications and challenges in big data analytics. J. Big Data 2: 1 (2015) - [j135]Richard Zuech, Taghi M. Khoshgoftaar, Randall Wald:
Intrusion detection and Big Heterogeneous Data: a Survey. J. Big Data 2: 3 (2015) - [j134]Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter, Hamzah Al Najada:
Survey of review spam detection using machine learning techniques. J. Big Data 2: 23 (2015) - [j133]Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter, Tawfiq Hasanin:
A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J. Big Data 2: 24 (2015) - [c282]Richard Zuech, Taghi M. Khoshgoftaar, Naeem Seliya, Maryam M. Najafabadi, Clifford Kemp:
A New Intrusion Detection Benchmarking System. FLAIRS 2015: 252-256 - [c281]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman:
Impact of Feature Selection Techniques for Tweet Sentiment Classification. FLAIRS 2015: 299-304 - [c280]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data. FLAIRS 2015: 329-334 - [c279]Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
The Effect of Dataset Size on Training Tweet Sentiment Classifiers. ICMLA 2015: 96-102 - [c278]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp:
Detection of SSH Brute Force Attacks Using Aggregated Netflow Data. ICMLA 2015: 283-288 - [c277]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Does the Inclusion of Data Sampling Improve the Performance of Boosting Algorithms on Imbalanced Bioinformatics Data? ICMLA 2015: 527-534 - [c276]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data. ICMLA 2015: 535-542 - [c275]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Investigating New Bootstrapping Approaches of Bagging Classifiers to Account for Class Imbalance in Bioinformatics Datasets. ICMLA 2015: 987-994 - [c274]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance. ICTAI 2015: 186-193 - [c273]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data? ICTAI 2015: 705-712 - [c272]Aaron N. Richter, Michael Crawford, Brian Heredia, Taghi M. Khoshgoftaar:
Efficient Modeling of User-Entity Preference in Big Social Networks. ICTAI 2015: 982-988 - [c271]Aaron N. Richter, Taghi M. Khoshgoftaar, Sara Landset, Tawfiq Hasanin:
A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data. IRI 2015: 1-8 - [c270]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Choosing an Appropriate Ensemble Classifier for Balanced Bioinformatics Data. IRI 2015: 17-24 - [c269]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data. IRI 2015: 197-202 - [c268]Gabriel Castaneda, Taghi M. Khoshgoftaar:
A Survey of 2D Face Databases. IRI 2015: 219-224 - [c267]Brian Heredia, Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman:
Building an Effective Classification Model for Breast Cancer Patient Response Data. IRI 2015: 229-235 - [c266]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman:
Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data. IRI 2015: 252-257 - [c265]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Alterations to the Bootstrapping Process within Random Forest: A Case Study on Imbalanced Bioinformatics Data. IRI 2015: 342-348 - [c264]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Ahmad Abu Shanab:
Observing the Effect of the Choice of Classifier on Bioinformatics Data with Varying Levels of Data Quality and Class Balance. IRI 2015: 372-379 - [c263]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data. IRI 2015: 457-463 - [c262]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Three Forms of Feature Selection Methods on Software Engineering Data. SEKE 2015: 385-390 - [c261]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Combining Feature Subset Selection and Data Sampling for Coping with Highly Imbalanced Software Data. SEKE 2015: 439-444 - 2014
- [j132]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. 24(9): 1229-1254 (2014) - [j131]Zhiwei Xu, Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya:
System regression test planning with a fuzzy expert system. Inf. Sci. 259: 532-543 (2014) - [j130]Taghi M. Khoshgoftaar, Yudong Xiao, Kehan Gao:
Software quality assessment using a multi-strategy classifier. Inf. Sci. 259: 555-570 (2014) - [j129]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco:
An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Inf. Sci. 259: 571-595 (2014) - [j128]Jason Van Hulse, Taghi M. Khoshgoftaar:
Incomplete-case nearest neighbor imputation in software measurement data. Inf. Sci. 259: 596-610 (2014) - [j127]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano, Randall Wald:
A comparative study of iterative and non-iterative feature selection techniques for software defect prediction. Inf. Syst. Frontiers 16(5): 801-822 (2014) - [j126]Matthew Herland, Taghi M. Khoshgoftaar, Randall Wald:
A review of data mining using big data in health informatics. J. Big Data 1: 2 (2014) - [c260]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald:
Evaluation of Wrapper-Based Feature Selection Using Hard, Moderate, and Easy Bioinformatics Data. BIBE 2014: 149-155 - [c259]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Correlation-Based Feature Selection for a Diverse Collection of Bioinformatics Datasets. BIBE 2014: 156-162 - [c258]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets. BIBE 2014: 304-310 - [c257]Randall Wald, Taghi M. Khoshgoftaar, Richard Zuech, Amri Napolitano:
Network Traffic Prediction Models for Near- and Long-Term Predictions. BIBE 2014: 362-368 - [c256]Charles Wheelus, Taghi M. Khoshgoftaar, Richard Zuech, Maryam M. Najafabadi:
A Session Based Approach for Aggregating Network Traffic Data - The SANTA Dataset. BIBE 2014: 369-378 - [c255]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Clifford Kemp, Naeem Seliya, Richard Zuech:
Machine Learning for Detecting Brute Force Attacks at the Network Level. BIBE 2014: 379-385 - [c254]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano, Alireza Fazelpour:
Select-Bagging: Effectively Combining Gene Selection and Bagging for Balanced Bioinformatics Data. BIBE 2014: 413-419 - [c253]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Effects of the Use of Boosting on Classification Performance of Imbalanced Bioinformatics Datasets. BIBE 2014: 420-426 - [c252]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data. FLAIRS 2014 - [c251]Kehan Gao, Taghi M. Khoshgoftaar, Randall Wald:
Combining Feature Selection and Ensemble Learning for Software Quality Estimation. FLAIRS 2014 - [c250]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Optimizing Wrapper-Based Feature Selection for Use on Bioinformatics Data. FLAIRS 2014 - [c249]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
How ranker and learner choice affects classification performance on noisy bioinformatics data. IRI 2014: 277-282 - [c248]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab:
The effect of noise level and distribution on classification of easy gene microarray data. IRI 2014: 297-302 - [c247]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of filter- and wrapper-based software metric selection techniques. IRI 2014: 309-314 - [c246]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Classification performance of three approaches for combining data sampling and gene selection on bioinformatics data. IRI 2014: 315-321 - [c245]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
Improving software quality estimation by combining feature selection strategies with sampled ensemble learning. IRI 2014: 428-433 - [c244]Gabriel Castaneda Oscos, Taghi M. Khoshgoftaar, Randall Wald:
Rotation invariant face recognition survey. IRI 2014: 835-840 - [c243]Randall Wald, Flavio Villanustre, Taghi M. Khoshgoftaar, Richard Zuech, Jarvis Robinson, Edin Muharemagic:
Using feature selection and classification to build effective and efficient firewalls. IRI 2014: 850-854 - [c242]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. SEKE 2014: 280-285 - [c241]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction. SEKE 2014: 540-545 - 2013
- [j125]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Selection for Optimization of Wavelet Packet Decomposition in Reliability Analysis of Systems. Int. J. Artif. Intell. Tools 22(5) (2013) - [j124]Huanjing Wang, Taghi M. Khoshgoftaar, Qianhui Althea Liang:
A Study of Software Metric Selection Techniques: stability Analysis and Defect Prediction Model Performance. Int. J. Artif. Intell. Tools 22(5) (2013) - [j123]Xingquan Zhu, Taghi M. Khoshgoftaar:
Editorial. Int. J. Artif. Intell. Tools 22(5) (2013) - [j122]Victor L. Winter, Bojan Cukic, Taghi M. Khoshgoftaar, Kinji Mori, Raymond A. Paul, Carlos Pérez Leguízamo, Sahra Sedigh Sarvestani, John C. Sloan, Mladen A. Vouk, I-Ling Yen:
High Consequence Systems and Semantic Computing. Int. J. Semantic Comput. 7(3): 291-324 (2013) - [c240]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Classification Performance of Rank Aggregation Techniques for Ensemble Gene Selection. FLAIRS 2013 - [c239]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman:
Ensemble Gene Selection Versus Single Gene Selection: Which Is Better? FLAIRS 2013 - [c238]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Improving Software Quality Estimation by Combining Boosting and Feature Selection. ICMLA (1) 2013: 27-33 - [c237]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study on Wrapper-Based Feature Selection for Software Engineering Data. ICMLA (2) 2013: 84-89 - [c236]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research. ICMLA (1) 2013: 154-160 - [c235]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Simplifying the Utilization of Machine Learning Techniques for Bioinformatics. ICMLA (2) 2013: 396-403 - [c234]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano:
Contrasting Undersampled Boosting with Internal and External Feature Selection for Patient Response Datasets. ICMLA (2) 2013: 404-410 - [c233]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab, Amri Napolitano:
Comparative Analysis on the Stability of Feature Selection Techniques Using Three Frameworks on Biological Datasets. ICMLA (1) 2013: 418-423 - [c232]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparison of Stability for Different Families of Filter-Based and Wrapper-Based Feature Selection. ICMLA (2) 2013: 457-464 - [c231]Matthew Herland, Taghi M. Khoshgoftaar, Randall Wald:
Survey of Clinical Data Mining Applications on Big Data in Health Informatics. ICMLA (2) 2013: 465-472 - [c230]Liz Aranguren Pachano, Taghi M. Khoshgoftaar, Randall Wald:
Survey of Data Cleansing and Monitoring for Large-Scale Battery Backup Installations. ICMLA (2) 2013: 478-484 - [c229]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Which Users Reply to and Interact with Twitter Social Bots? ICTAI 2013: 135-144 - [c228]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Filter- and Wrapper-Based Feature Subset Selection. ICTAI 2013: 374-380 - [c227]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Wael Awada:
A Review of Ensemble Classification for DNA Microarrays Data. ICTAI 2013: 381-389 - [c226]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation. ICTAI 2013: 426-432 - [c225]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Should the Same Learners Be Used Both within Wrapper Feature Selection and for Building Classification Models? ICTAI 2013: 439-445 - [c224]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Maximizing Classification Performance for Patient Response Datasets. ICTAI 2013: 454-462 - [c223]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab:
Comparison of Two Frameworks for Measuring the Stability of Gene-Selection Techniques on Noisy Class-Imbalanced Data. ICTAI 2013: 881-888 - [c222]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Predicting susceptibility to social bots on Twitter. IRI 2013: 6-13 - [c221]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
The importance of performance metrics within wrapper feature selection. IRI 2013: 105-111 - [c220]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparison of rank-based vs. score-based aggregation for ensemble gene selection. IRI 2013: 225-231 - [c219]Randall Wald, Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman:
Hidden dependencies between class imbalance and difficulty of learning for bioinformatics datasets. IRI 2013: 232-238 - [c218]Randall Wald, Taghi M. Khoshgoftaar:
Patient response datasets: Challenges and opportunities. IRI 2013: 254-261 - [c217]Taghi M. Khoshgoftaar, Randall Wald, David J. Dittman, Amri Napolitano:
Feature list aggregation approaches for ensemble gene selection on patient response datasets. IRI 2013: 317-324 - [c216]Randall Wald, Taghi M. Khoshgoftaar, Alireza Fazelpour:
The use of balance-aware subsampling for bioinformatics datasets. IRI 2013: 325-332 - [c215]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Gene selection stability's dependence on dataset difficulty. IRI 2013: 341-348 - [c214]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Filter- and wrapper-based feature selection for predicting user interaction with Twitter bots. IRI 2013: 416-423 - [c213]Taghi M. Khoshgoftaar, Alireza Fazelpour, Huanjing Wang, Randall Wald:
A survey of stability analysis of feature subset selection techniques. IRI 2013: 424-431 - [c212]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A Study on First Order Statistics-Based Feature Selection Techniques on Software Metric Data. SEKE 2013: 467-472 - [c211]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation. SEKE 2013: 612-617 - [c210]Taghi M. Khoshgoftaar:
Overcoming Big Data Challenges. SEKE 2013: xxix - 2012
- [j121]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
Measuring stability of feature ranking techniques: a noise-based approach. Int. J. Bus. Intell. Data Min. 7(1/2): 80-115 (2012) - [j120]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse:
Evaluation of the importance of data pre-processing order when combining feature selection and data sampling. Int. J. Bus. Intell. Data Min. 7(1/2): 116-134 (2012) - [j119]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang:
Exploring filter-based feature selection techniques for software quality classification. Int. J. Inf. Decis. Sci. 4(2/3): 217-250 (2012) - [j118]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Software measurement data reduction using ensemble techniques. Neurocomputing 92: 124-132 (2012) - [j117]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
An Empirical Study of Feature Ranking Techniques for Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. 22(2): 161-183 (2012) - [j116]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald:
Threshold-based feature selection techniques for high-dimensional bioinformatics data. Netw. Model. Anal. Health Informatics Bioinform. 1(1-2): 47-61 (2012) - [j115]Ankur Agarwal, Georgiana L. Hamza-Lup, Taghi M. Khoshgoftaar:
A System-Level Modeling Methodology for Performance-Driven Component Selection in Multicore Architectures. IEEE Syst. J. 6(2): 317-328 (2012) - [j114]Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya:
Predicting high-risk program modules by selecting the right software measurements. Softw. Qual. J. 20(1): 3-42 (2012) - [c209]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Similarity analysis of feature ranking techniques on imbalanced DNA microarray datasets. BIBM 2012: 1-5 - [c208]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab:
The effect of measurement approach and noise level on gene selection stability. BIBM 2012: 1-5 - [c207]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald:
Robustness of Threshold-Based Feature Rankers with Data Sampling on Noisy and Imbalanced Data. FLAIRS 2012 - [c206]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman:
Mean Aggregation versus Robust Rank Aggregation for Ensemble Gene Selection. ICMLA (1) 2012: 63-69 - [c205]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Determining the Number of Iterations Appropriate for Ensemble Gene Selection on Microarray Data. ICMLA (1) 2012: 82-89 - [c204]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Alireza Fazelpour:
First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques. ICMLA (2) 2012: 151-157 - [c203]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman:
A New Fixed-Overlap Partitioning Algorithm for Determining Stability of Bioinformatics Gene Rankers. ICMLA (2) 2012: 170-177 - [c202]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparing Two New Gene Selection Ensemble Approaches with the Commonly-Used Approach. ICMLA (2) 2012: 184-191 - [c201]Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald:
The Effect of Number of Iterations on Ensemble Gene Selection. ICMLA (2) 2012: 198-203 - [c200]Janell Duhaney, Taghi M. Khoshgoftaar, Amri Napolitano:
Studying the Effect of Class Imbalance in Ocean Turbine Fault Data on Reliable State Detection. ICMLA (1) 2012: 268-275 - [c199]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
A Hybrid Approach to Coping with High Dimensionality and Class Imbalance for Software Defect Prediction. ICMLA (2) 2012: 281-288 - [c198]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A Comparative Study on the Stability of Software Metric Selection Techniques. ICMLA (2) 2012: 301-307 - [c197]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study on the Stability of Feature Selection for Imbalanced Software Engineering Data. ICMLA (1) 2012: 317-323 - [c196]Janell Duhaney, Taghi M. Khoshgoftaar, Randall Wald:
Applying Feature Selection to Short Time Wavelet Transformed Vibration Data for Reliability Analysis of an Ocean Turbine. ICMLA (1) 2012: 330-337 - [c195]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Using Twitter Content to Predict Psychopathy. ICMLA (2) 2012: 394-401 - [c194]Janell Duhaney, Taghi M. Khoshgoftaar:
Decision Level Fusion of Wavelet Features for Ocean Turbine State Detection. ICMLA (2) 2012: 531-537 - [c193]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data. ICMLA (2) 2012: 551-557 - [c192]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A novel dataset-similarity-aware approach for evaluating stability of software metric selection techniques. IRI 2012: 1-8 - [c191]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
Exploring an iterative feature selection technique for highly imbalanced data sets. IRI 2012: 101-108 - [c190]Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano:
A review of the stability of feature selection techniques for bioinformatics data. IRI 2012: 356-363 - [c189]Gordon K. Lee, Elisa Bertino, Stuart Harvey Rubin, Taghi M. Khoshgoftaar, Bhavani Thuraisingham, James D. McCaffrey:
Panel: Using information re-use and integration principles in big data. IRI 2012 - [c188]Randall Wald, Taghi M. Khoshgoftaar, Chris Sumner:
Machine prediction of personality from Facebook profiles. IRI 2012: 109-115 - [c187]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Wael Awada, Amri Napolitano:
An extensive comparison of feature ranking aggregation techniques in bioinformatics. IRI 2012: 377-384 - [c186]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Impact of noise and data sampling on stability of feature ranking techniques for biological datasets. IRI 2012: 415-422 - [c185]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Filter-Based Feature Selection Methods for Imbalanced Software Measurement Data. SEKE 2012: 74-79 - [c184]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
An Empirical Study of Software Metric Selection Techniques for Defect Prediction. SEKE 2012: 94-99 - 2011
- [j113]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An exploration of learning when data is noisy and imbalanced. Intell. Data Anal. 15(2): 215-236 (2011) - [j112]Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse, Kehan Gao:
Metric Selection for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. 21(2): 237-257 (2011) - [j111]Alex Kotlarchyk, Taghi M. Khoshgoftaar, Mirjana Pavlovic, Hanqi Zhuang, Abhijit S. Pandya:
Identification of microRNA biomarkers for cancer by combining multiple feature selection techniques. J. Comput. Methods Sci. Eng. 11(5-6): 283-298 (2011) - [j110]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Evaluating the Impact of Data Quality on Sampling. J. Inf. Knowl. Manag. 10(3): 225-245 (2011) - [j109]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang, Naeem Seliya:
Choosing software metrics for defect prediction: an investigation on feature selection techniques. Softw. Pract. Exp. 41(5): 579-606 (2011) - [j108]Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data. IEEE Trans. Syst. Man Cybern. Part A 41(3): 552-568 (2011) - [j107]Qianhui Liang, Xindong Wu, E. K. Park, Taghi M. Khoshgoftaar, Chi-Hung Chi:
Ontology-Based Business Process Customization for Composite Web Services. IEEE Trans. Syst. Man Cybern. Part A 41(4): 717-729 (2011) - [j106]Naeem Seliya, Taghi M. Khoshgoftaar:
The use of decision trees for cost-sensitive classification: an empirical study in software quality prediction. WIREs Data Mining Knowl. Discov. 1(5): 448-459 (2011) - [c183]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Huanjing Wang:
Stability Analysis of Feature Ranking Techniques on Biological Datasets. BIBM 2011: 252-256 - [c182]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Random forest: A reliable tool for patient response prediction. BIBM Workshops 2011: 289-296 - [c181]Wilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse:
Robustness of Filter-Based Feature Ranking: A Case Study. FLAIRS 2011 - [c180]Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Level Sensor Fusion for Improved Fault Detection in MCM Systems for Ocean Turbines. FLAIRS 2011 - [c179]Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya:
How Many Software Metrics Should be Selected for Defect Prediction? FLAIRS 2011 - [c178]John C. Sloan, Taghi M. Khoshgoftaar:
Ensemble Coordination for Discrete Event Control. HASE 2011: 227-235 - [c177]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Using Feature Selection to Determine Optimal Depth for Wavelet Packet Decomposition of Vibration Signals for Ocean System Reliability. HASE 2011: 236-243 - [c176]Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan, Bassem Alhalabi, Pierre P. Beaujean:
A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring. HASE 2011: 244-251 - [c175]Huanjing Wang, Taghi M. Khoshgoftaar, Qianhui Althea Liang:
Stability and Classification Performance of Feature Selection Techniques. ICMLA (1) 2011: 151-156 - [c174]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald:
Impact of Noise and Data Sampling on Stability of Feature Selection. ICMLA (1) 2011: 172-177 - [c173]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Selection for Vibration Sensor Data Transformed by a Streaming Wavelet Packet Decomposition. ICTAI 2011: 978-985 - [c172]Huanjing Wang, Taghi M. Khoshgoftaar:
Measuring Stability of Threshold-Based Feature Selection Techniques. ICTAI 2011: 986-993 - [c171]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Impact of Data Sampling on Stability of Feature Selection for Software Measurement Data. ICTAI 2011: 1004-1011 - [c170]Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Selection on Dynamometer Data for Reliability Analysis. ICTAI 2011: 1012-1019 - [c169]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse:
Comparison of approaches to alleviate problems with high-dimensional and class-imbalanced data. IRI 2011: 234-239 - [c168]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
A noise-based stability evaluation of threshold-based feature selection techniques. IRI 2011: 240-245 - [c167]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald:
Measuring robustness of Feature Selection techniques on software engineering datasets. IRI 2011: 309-314 - [c166]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A comparative evaluation of feature ranking methods for high dimensional bioinformatics data. IRI 2011: 315-320 - [c165]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Fourier transforms for vibration analysis: A review and case study. IRI 2011: 366-371 - [c164]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Classifier-Based Nominal Imputation to Improve Machine Learning. PAKDD (1) 2011: 124-135 - [c163]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
A Comparative Study of Different Strategies for Predicting Software Quality. SEKE 2011: 65-70 - [c162]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study of Software Metrics Selection Using Support Vector Machine. SEKE 2011: 83-88 - [c161]Kehan Gao, Taghi M. Khoshgoftaar:
Software Defect Prediction for High-Dimensional and Class-Imbalanced Data. SEKE 2011: 89-94 - [e3]Taghi M. Khoshgoftaar:
13th IEEE International Symposium on High-Assurance Systems Engineering, HASE 2011, Boca Raton, FL, USA, November 10-12, 2011. IEEE Computer Society 2011, ISBN 978-1-4673-0107-7 [contents] - 2010
- [j105]Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown:
Evolutionary data analysis for the class imbalance problem. Intell. Data Anal. 14(1): 69-88 (2010) - [j104]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Evaluation of Repetitive Undersampling Techniques. Int. J. Softw. Eng. Knowl. Eng. 20(2): 173-195 (2010) - [j103]Lianfen Qian, Qingchuan Yao, Taghi M. Khoshgoftaar:
Dynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software. J. Softw. Eng. Appl. 3(6): 603-609 (2010) - [j102]Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors. IEEE Trans. Neural Networks 21(5): 813-830 (2010) - [j101]Yi Liu, Taghi M. Khoshgoftaar, Naeem Seliya:
Evolutionary Optimization of Software Quality Modeling with Multiple Repositories. IEEE Trans. Software Eng. 36(6): 852-864 (2010) - [j100]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE Trans. Syst. Man Cybern. Part A 40(1): 185-197 (2010) - [c160]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
An Empirical Study of Predictive Modeling Techniques of Software Quality. BIONETICS 2010: 288-302 - [c159]Kehan Gao, Taghi M. Khoshgoftaar, Jason Van Hulse:
An Evaluation of Sampling on Filter-Based Feature Selection Methods. FLAIRS 2010 - [c158]Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse:
A Comparative Study of Threshold-Based Feature Selection Techniques. GrC 2010: 499-504 - [c157]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
Predicting Faults in High Assurance Software. HASE 2010: 26-34 - [c156]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A Novel Noise Filtering Algorithm for Imbalanced Data. ICMLA 2010: 9-14 - [c155]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction. ICMLA 2010: 135-140 - [c154]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse:
Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques. ICMLA 2010: 147-152 - [c153]Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction. ICTAI (1) 2010: 137-144 - [c152]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Evaluating the impact of data quality on sampling. IRI 2010: 31-36 - [c151]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao:
A comparative study of filter-based feature ranking techniques. IRI 2010: 43-48 - [c150]Naeem Seliya, Taghi M. Khoshgoftaar:
Active learning with neural networks for intrusion detection. IRI 2010: 49-54 - [c149]Taghi M. Khoshgoftaar, Kehan Gao, Jason Van Hulse:
A novel feature selection technique for highly imbalanced data. IRI 2010: 80-85 - [c148]Taghi M. Khoshgoftaar, Kehan Gao:
Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves. SEKE 2010: 203-208 - [c147]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao:
Ensemble Feature Selection Technique for Software Quality Classification. SEKE 2010: 215-220 - [e2]Sorin Draghici, Taghi M. Khoshgoftaar, Vasile Palade, Witold Pedrycz, M. Arif Wani, Xingquan Zhu:
The Ninth International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, USA, 12-14 December 2010. IEEE Computer Society 2010, ISBN 978-0-7695-4300-0 [contents]
2000 – 2009
- 2009
- [j99]Xiaoyuan Su, Taghi M. Khoshgoftaar:
A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell. 2009: 421425:1-421425:19 (2009) - [j98]Jason Van Hulse, Taghi M. Khoshgoftaar:
Knowledge discovery from imbalanced and noisy data. Data Knowl. Eng. 68(12): 1513-1542 (2009) - [j97]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse:
Hybrid sampling for imbalanced data. Integr. Comput. Aided Eng. 16(3): 193-210 (2009) - [j96]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Making an accurate classifier ensemble by voting on classifications from imputed learning sets. Int. J. Inf. Decis. Sci. 1(3): 301-322 (2009) - [j95]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Identifying Learners Robust to Low Quality Data. Informatica (Slovenia) 33(3): 245-259 (2009) - [j94]Taghi M. Khoshgoftaar, Pierre Rebours, Naeem Seliya:
Software quality analysis by combining multiple projects and learners. Softw. Qual. J. 17(1): 25-49 (2009) - [j93]John C. Sloan, Taghi M. Khoshgoftaar:
From Web Service Artifact to a Readable and Verifiable Model. IEEE Trans. Serv. Comput. 2(4): 277-288 (2009) - [j92]Taghi M. Khoshgoftaar, Jason Van Hulse:
Empirical Case Studies in Attribute Noise Detection. IEEE Trans. Syst. Man Cybern. Part C 39(4): 379-388 (2009) - [j91]Dennis J. Drown, Taghi M. Khoshgoftaar, Naeem Seliya:
Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems. IEEE Trans. Syst. Man Cybern. Part A 39(5): 1097-1107 (2009) - [j90]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse:
Improving Software-Quality Predictions With Data Sampling and Boosting. IEEE Trans. Syst. Man Cybern. Part A 39(6): 1283-1294 (2009) - [c146]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
VipBoost: A More Accurate Boosting Algorithm. FLAIRS 2009 - [c145]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald:
Feature Selection with High-Dimensional Imbalanced Data. ICDM Workshops 2009: 507-514 - [c144]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
Mining Data from Multiple Software Development Projects. ICDM Workshops 2009: 551-557 - [c143]Taghi M. Khoshgoftaar, Kehan Gao:
Feature Selection with Imbalanced Data for Software Defect Prediction. ICMLA 2009: 235-240 - [c142]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
Wrapper-Based Feature Ranking for Software Engineering Metrics. ICMLA 2009: 241-246 - [c141]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
A Study on the Relationships of Classifier Performance Metrics. ICTAI 2009: 59-66 - [c140]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Exploring Software Quality Classification with a Wrapper-Based Feature Ranking Technique. ICTAI 2009: 67-74 - [c139]Wilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse:
An Empirical Study on Wrapper-Based Feature Ranking. ICTAI 2009: 75-82 - [c138]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
High-Dimensional Software Engineering Data and Feature Selection. ICTAI 2009: 83-90 - [c137]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Comparison of Repetitive Undersampling Techniques. IRI 2009: 29-34 - [c136]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
Aggregating Performance Metrics for Classifier Evaluation. IRI 2009: 35-40 - [c135]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang:
An Empirical Investigation of Filter Attribute Selection Techniques for Software Quality Classification. IRI 2009: 272-277 - [c134]Pengpeng Lin, Huanjing Wang, Taghi M. Khoshgoftaar:
A Novel Hybrid Search Algorithm for Feature Selection. SEKE 2009: 81-86 - [c133]Naeem Seliya, Taghi M. Khoshgoftaar:
Value-Based Software Quality Modeling. SEKE 2009: 116-121 - [c132]John C. Sloan, Taghi M. Khoshgoftaar, Augusto Varas:
An Extendible Translation of BPEL to a Machine-verifiable Model. SEKE 2009: 344-349 - [r1]Kehan Gao, Taghi M. Khoshgoftaar:
Count Models for Software Quality Estimation. Encyclopedia of Data Warehousing and Mining 2009: 346-352 - 2008
- [j89]John C. Sloan, Taghi M. Khoshgoftaar, Venkat Raghav:
Assuring Timeliness in an e-Science Service-Oriented Architecture. Computer 41(8): 56-62 (2008) - [j88]Xiaoyuan Su, Taghi M. Khoshgoftaar:
Collaborative Filtering for Multi-Class Data Using Bayesian Networks. Int. J. Artif. Intell. Tools 17(1): 71-85 (2008) - [j87]Taghi M. Khoshgoftaar, Naeem Seliya, Chris Seiffert:
Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. Int. J. Artif. Intell. Tools 17(3): 521-537 (2008) - [j86]Jason Van Hulse, Taghi M. Khoshgoftaar:
A comprehensive empirical evaluation of missing value imputation in noisy software measurement data. J. Syst. Softw. 81(5): 691-708 (2008) - [j85]Taghi M. Khoshgoftaar, Jason Van Hulse:
Imputation techniques for multivariate missingness in software measurement data. Softw. Qual. J. 16(4): 563-600 (2008) - [c131]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard:
Software quality modeling: The impact of class noise on the random forest classifier. IEEE Congress on Evolutionary Computation 2008: 3853-3859 - [c130]Chinar C. Shah, Xingquan Zhu, Taghi M. Khoshgoftaar, Justin Beyer:
Contrast Pattern Mining with Gap Constraints for Peptide Folding Prediction. FLAIRS 2008: 95-100 - [c129]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Building Useful Models from Imbalanced Data with Sampling and Boosting. FLAIRS 2008: 306-311 - [c128]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
A Mixture Imputation-Boosted Collaborative Filter. FLAIRS 2008: 312-316 - [c127]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
A Comparative Study of Data Sampling and Cost Sensitive Learning. ICDM Workshops 2008: 46-52 - [c126]Andres Folleco, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparison of Four Performance Metrics for Evaluating Sampling Techniques for Low Quality Class-Imbalanced Data. ICMLA 2008: 153-158 - [c125]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
RUSBoost: Improving classification performance when training data is skewed. ICPR 2008: 1-4 - [c124]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu:
VoB predictors: Voting on bagging classifications. ICPR 2008: 1-4 - [c123]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Using Imputation Techniques to Help Learn Accurate Classifiers. ICTAI (1) 2008: 437-444 - [c122]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Resampling or Reweighting: A Comparison of Boosting Implementations. ICTAI (1) 2008: 445-451 - [c121]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Improving Learner Performance with Data Sampling and Boosting. ICTAI (1) 2008: 452-459 - [c120]Naeem Seliya, Zhiwei Xu, Taghi M. Khoshgoftaar:
Addressing Class Imbalance in Non-binary Classification Problems. ICTAI (1) 2008: 460-466 - [c119]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard:
Identifying learners robust to low quality data. IRI 2008: 190-195 - [c118]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse:
Hybrid sampling for imbalanced data. IRI 2008: 202-207 - [c117]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu:
VCI predictors: Voting on classifications from imputed learning sets. IRI 2008: 296-301 - [c116]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu, Russell Greiner:
Imputation-boosted collaborative filtering using machine learning classifiers. SAC 2008: 949-950 - [c115]Andres Folleco, Taghi M. Khoshgoftaar, Lofton A. Bullard:
Analyzing the Impact of Attribute Noise on Software Quality Classification. SEKE 2008: 73-78 - [c114]Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown:
On the Rarity of Fault-prone Modules in Knowledge-based Software Quality Modeling. SEKE 2008: 279-284 - [c113]John C. Sloan, Taghi M. Khoshgoftaar:
Toward Model Checking Web Services Over the Web. SEKE 2008: 519-524 - [c112]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Imputed Neighborhood Based Collaborative Filtering. Web Intelligence 2008: 633-639 - [p2]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Module Risk Analysis. Wiley Encyclopedia of Computer Science and Engineering 2008 - [p1]Naeem Seliya, Taghi M. Khoshgoftaar:
Software Quality Modeling as a Reliability Tool. Wiley Encyclopedia of Computer Science and Engineering 2008 - 2007
- [j84]Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert, Lili Zhao:
The multiple imputation quantitative noise corrector. Intell. Data Anal. 11(3): 245-263 (2007) - [j83]Taghi M. Khoshgoftaar, Pierre Rebours:
Improving Software Quality Prediction by Noise Filtering Techniques. J. Comput. Sci. Technol. 22(3): 387-396 (2007) - [j82]Xingquan Zhu, Taghi M. Khoshgoftaar, Ian Davidson, Shichao Zhang:
Editorial: Special issue on mining low-quality data. Knowl. Inf. Syst. 11(2): 131-136 (2007) - [j81]Jason Van Hulse, Taghi M. Khoshgoftaar, Haiying Huang:
The pairwise attribute noise detection algorithm. Knowl. Inf. Syst. 11(2): 171-190 (2007) - [j80]Naeem Seliya, Taghi M. Khoshgoftaar:
Software quality estimation with limited fault data: a semi-supervised learning perspective. Softw. Qual. J. 15(3): 327-344 (2007) - [j79]Taghi M. Khoshgoftaar, Kehan Gao:
Count Models for Software Quality Estimation. IEEE Trans. Reliab. 56(2): 212-222 (2007) - [j78]Kehan Gao, Taghi M. Khoshgoftaar:
A Comprehensive Empirical Study of Count Models for Software Fault Prediction. IEEE Trans. Reliab. 56(2): 223-236 (2007) - [j77]Taghi M. Khoshgoftaar, Yi Liu:
A Multi-Objective Software Quality Classification Model Using Genetic Programming. IEEE Trans. Reliab. 56(2): 237-245 (2007) - [j76]Naeem Seliya, Taghi M. Khoshgoftaar:
Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering. IEEE Trans. Syst. Man Cybern. Part A 37(2): 201-211 (2007) - [c111]Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya:
Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. FLAIRS 2007: 490-495 - [c110]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Skewed Class Distributions and Mislabeled Examples. ICDM Workshops 2007: 477-482 - [c109]Xiaoyuan Su, Taghi M. Khoshgoftaar:
Arbitrarily-Shaped Window Based Stereo Matching using the Go-Light Optimization Algorithm. ICIP (6) 2007: 556-559 - [c108]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Experimental perspectives on learning from imbalanced data. ICML 2007: 935-942 - [c107]Lofton A. Bullard, Taghi M. Khoshgoftaar, Kehan Gao:
An application of a rule-based model in software quality classification. ICMLA 2007: 204-210 - [c106]Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse, Amri Napolitano, Andres Folleco:
Learning with limited minority class data. ICMLA 2007: 348-353 - [c105]Dennis J. Drown, Taghi M. Khoshgoftaar, Ramaswamy Narayanan:
Using evolutionary sampling to mine imbalanced data. ICMLA 2007: 363-368 - [c104]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Mining Data with Rare Events: A Case Study. ICTAI (2) 2007: 132-139 - [c103]Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse:
An Empirical Study of Learning from Imbalanced Data Using Random Forest. ICTAI (2) 2007: 310-317 - [c102]Xingquan Zhu, Xindong Wu, Taghi M. Khoshgoftaar, Yong Shi:
An Empirical Study of the Noise Impact on Cost-Sensitive Learning. IJCAI 2007: 1168-1174 - [c101]Qiming Luo, Taghi M. Khoshgoftaar:
An Empirical Study on Estimating Motions in Video Stabilization. IRI 2007: 360-366 - [c100]Jason Van Hulse, Taghi M. Khoshgoftaar:
Incomplete-Case Nearest Neighbor Imputation in Software Measurement Data. IRI 2007: 630-637 - [c99]Yi Liu, Taghi M. Khoshgoftaar, Jenq-Foung Yao:
Building a Novel GP-Based Software Quality Classifier Using Multiple Validation Datasets. IRI 2007: 644-650 - [c98]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco:
An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. IRI 2007: 651-658 - [c97]Xiaoyuan Su, Taghi M. Khoshgoftaar:
A Progressive Edge-Based Stereo Correspondence Method. ISVC (1) 2007: 248-257 - [c96]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu, Andres Folleco:
Rule-Based Multiple Object Tracking for Traffic Surveillance Using Collaborative Background Extraction. ISVC (2) 2007: 469-478 - [c95]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert:
Learning from Software Quality Data with Class Imbalance and Noise. SEKE 2007: 487- - [c94]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Xingquan Zhu:
Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts. Web Intelligence 2007: 645-649 - 2006
- [j75]Pierre Rebours, Taghi M. Khoshgoftaar:
Quality Problem in Software Measurement Data. Adv. Comput. 66: 44-79 (2006) - [j74]Taghi M. Khoshgoftaar, Jason Van Hulse:
Determining noisy instances relative to attributes of interest. Intell. Data Anal. 10(3): 251-268 (2006) - [j73]Jason Van Hulse, Taghi M. Khoshgoftaar:
Class noise detection using frequent itemsets. Intell. Data Anal. 10(6): 487-507 (2006) - [j72]Taghi M. Khoshgoftaar, Kehan Gao, Hua Lin:
Indirect classification approaches: a comparative study in network intrusion detection. Int. J. Comput. Appl. Technol. 27(4): 232-245 (2006) - [j71]Taghi M. Khoshgoftaar, Pierre Rebours:
Noise elimination with partitioning filter for software quality estimation. Int. J. Comput. Appl. Technol. 27(4): 246-258 (2006) - [j70]Taghi M. Khoshgoftaar, Vedang H. Joshi, Naeem Seliya:
Detecting Noisy Instances with the Ensemble Filter: a Study in Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. 16(1): 53-76 (2006) - [j69]Taghi M. Khoshgoftaar, Naeem Seliya, Nandini Sundaresh:
An empirical study of predicting software faults with case-based reasoning. Softw. Qual. J. 14(2): 85-111 (2006) - [j68]Taghi M. Khoshgoftaar, Angela Herzberg, Naeem Seliya:
Resource oriented selection of rule-based classification models: An empirical case study. Softw. Qual. J. 14(4): 309-338 (2006) - [j67]Qiming Luo, Taghi M. Khoshgoftaar:
Unsupervised multiscale color image segmentation based on MDL principle. IEEE Trans. Image Process. 15(9): 2755-2761 (2006) - [c93]Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert:
A Comparison of Software Fault Imputation Procedures. ICMLA 2006: 135-142 - [c92]Xiaoyuan Su, Taghi M. Khoshgoftaar:
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms. ICTAI 2006: 497-504 - [c91]Taghi M. Khoshgoftaar, Kehan Gao:
Assessment of a Multi-Strategy Classifier for an Embedded Software System. ICTAI 2006: 651-658 - [c90]Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert:
A Hybrid Approach to Cleansing Software Measurement Data. ICTAI 2006: 713-722 - [c89]Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya:
Labeling network event records for intrusion detection in a Wireless LAN. IRI 2006: 200-206 - [c88]Yi Liu, Taghi M. Khoshgoftaar, Jenq-Foung Yao:
Developing an effective validation strategy for genetic programming models based on multiple datasets. IRI 2006: 232-237 - [c87]Qiming Luo, Taghi M. Khoshgoftaar, Andres Folleco:
Classification of ships in surveillance video. IRI 2006: 432-437 - [c86]Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert, Lili Zhao:
Noise correction using bayesian multiple imputation. IRI 2006: 478-483 - [c85]Taghi M. Khoshgoftaar, Andres Folleco, Jason Van Hulse, Lofton A. Bullard:
Software quality imputation in the presence of noisy data. IRI 2006: 484-489 - [c84]Taghi M. Khoshgoftaar, Jason Van Hulse:
Multiple Imputation of Software Measurement Data: A Case Study. SEKE 2006: 220-226 - [c83]Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse:
Polishing Noise in Continuous Software Measurement Data. SEKE 2006: 227-231 - 2005
- [j66]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study. Empir. Softw. Eng. 10(2): 183-218 (2005) - [j65]Taghi M. Khoshgoftaar, Shi Zhong, Vedang H. Joshi:
Enhancing software quality estimation using ensemble-classifier based noise filtering. Intell. Data Anal. 9(1): 3-27 (2005) - [j64]Taghi M. Khoshgoftaar, Kehan Gao, Nawal H. Ibrahim:
Evaluating indirect and direct classification techniques for network intrusion detection. Intell. Data Anal. 9(3): 309-326 (2005) - [j63]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Detecting noisy instances with the rule-based classification model. Intell. Data Anal. 9(4): 347-364 (2005) - [j62]Taghi M. Khoshgoftaar, Pierre Rebours:
Evaluating noise elimination techniques for software quality estimation. Intell. Data Anal. 9(5): 487-508 (2005) - [j61]Taghi M. Khoshgoftaar, Jason Van Hulse:
Identifying noisy features with the Pairwise Attribute Noise Detection Algorithm. Intell. Data Anal. 9(6): 589-602 (2005) - [j60]Taghi M. Khoshgoftaar, Kehan Gao, Robert M. Szabo:
Comparing software fault predictions of pure and zero-inflated Poisson regression models. Int. J. Syst. Sci. 36(11): 705-715 (2005) - [j59]Taghi M. Khoshgoftaar, Naeem Seliya, Angela Herzberg:
Resource-oriented software quality classification models. J. Syst. Softw. 76(2): 111-126 (2005) - [c82]Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong:
Analyzing Software Quality with Limited Fault-Proneness Defect Data. HASE 2005: 89-98 - [c81]Taghi M. Khoshgoftaar, Jason Van Hulse:
Identifying noise in an attribute of interest. ICMLA 2005 - [c80]Taghi M. Khoshgoftaar, Shyam Varan Nath, Shi Zhong, Naeem Seliya:
Intrusion detection in wireless networks using clustering techniques with expert analysis. ICMLA 2005 - [c79]Shi Zhong, Taghi M. Khoshgoftaar, Shyam Varan Nath:
A Clustering Approach to Wireless Network Intrusion Detection. ICTAI 2005: 190-196 - [c78]Zhiwei Xu, Kehan Gao, Taghi M. Khoshgoftaar:
Application of fuzzy expert system in test case selection for system regression test. IRI 2005: 120-125 - [c77]Yudong Xiao, Taghi M. Khoshgoftaar, Naeem Seliya:
The partitioning- and rule-based filter for noise detection. IRI 2005: 205-210 - [c76]Taghi M. Khoshgoftaar, Jason Van Hulse:
Empirical case studies in attribute noise detection. IRI 2005: 211-216 - [c75]Qiming Luo, Taghi M. Khoshgoftaar, Edgar An:
Hierarchical indexing of ocean survey video by mean shift clustering and MDL principle. IRI 2005: 404-409 - [e1]Du Zhang, Taghi M. Khoshgoftaar, Mei-Ling Shyu:
Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005, August 15-17, 2005, Las Vegas Hilton, Las Vegas, NV, USA. IEEE Systems, Man, and Cybernetics Society 2005, ISBN 0-7803-9093-8 [contents] - 2004
- [j58]Taghi M. Khoshgoftaar, Naeem Seliya:
Software quality estimation with case-based reasoning. Adv. Comput. 62: 249-291 (2004) - [j57]Taghi M. Khoshgoftaar, Naeem Seliya:
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study. Empir. Softw. Eng. 9(3): 229-257 (2004) - [j56]Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya:
Analyzing Software Measurement Data with Clustering Techniques. IEEE Intell. Syst. 19(2): 20-27 (2004) - [j55]Zhiwei Xu, Taghi M. Khoshgoftaar:
Identification of fuzzy models of software cost estimation. Fuzzy Sets Syst. 145(1): 141-163 (2004) - [j54]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
A multiobjective module-order model for software quality enhancement. IEEE Trans. Evol. Comput. 8(6): 593-608 (2004) - [c74]Yi Liu, Taghi M. Khoshgoftaar:
Reducing Overfitting in Genetic Programming Models for Software Quality Classification. HASE 2004: 56-65 - [c73]Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya:
Unsupervised Learning for Expert-Based Software Quality Estimation. HASE 2004: 149-155 - [c72]Taghi M. Khoshgoftaar, Mohamed E. Abushadi:
Resource-Sensitive Intrusion Detection Models for Network Traffic. HASE 2004: 249-258 - [c71]Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong:
Semi-Supervised Learning for Software Quality Estimation. ICTAI 2004: 183-190 - [c70]Qiming Luo, Taghi M. Khoshgoftaar:
Efficient Image Segmentation by Mean Shift Clustering and MDL-Guided Region Merging. ICTAI 2004: 337-343 - [c69]Wei Tang, Taghi M. Khoshgoftaar:
Noise Identification with the k-Means Algorithm. ICTAI 2004: 373-378 - [c68]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Rule-Based Noise Detection for Software Measurement Data. IRI 2004: 302-307 - [c67]Taghi M. Khoshgoftaar, Pierre Rebours:
Generating Multiple Noise Elimination Filters with the Ensemble-Partitioning Filter. IRI 2004: 369-375 - [c66]Taghi M. Khoshgoftaar, Naeem Seliya:
The Necessity of Assuring Quality in Software Measurement Data. IEEE METRICS 2004: 119-130 - [c65]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach. IEEE METRICS 2004: 159-169 - [c64]Taghi M. Khoshgoftaar, Yudong Xiao, Kehan Gao:
Multi-Objective Optimization by CBR GA-Optimizer for Module-Order Modeling. SEKE 2004: 220-225 - [c63]Taghi M. Khoshgoftaar, Vedang H. Joshi:
Noise Elimination with Ensemble-Classifier Filtering: A Case-Study in Software Quality Engineerin. SEKE 2004: 226-231 - 2003
- [j53]Taghi M. Khoshgoftaar, Naeem Seliya:
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques. Empir. Softw. Eng. 8(3): 255-283 (2003) - [j52]Taghi M. Khoshgoftaar, Naeem Seliya:
Analogy-Based Practical Classification Rules for Software Quality Estimation. Empir. Softw. Eng. 8(4): 325-350 (2003) - [j51]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm. Int. J. Artif. Intell. Tools 12(3): 207-225 (2003) - [j50]Zhiwei Xu, Taghi M. Khoshgoftaar, Edward B. Allen:
Application of fuzzy expert systems in assessing operational risk of software. Inf. Softw. Technol. 45(7): 373-388 (2003) - [j49]Taghi M. Khoshgoftaar, Edward B. Allen:
Ordering Fault-Prone Software Modules. Softw. Qual. J. 11(1): 19-37 (2003) - [j48]Taghi M. Khoshgoftaar:
Introduction to the Special Issue on Quality Engineering with Computational Intelligence. Softw. Qual. J. 11(2): 85-86 (2003) - [c62]Yi Liu, Taghi M. Khoshgoftaar:
Building Decision Tree Software Quality Classification Models Using Genetic Programming. GECCO 2003: 1808-1809 - [c61]Taghi M. Khoshgoftaar, Lofton A. Bullard, Kehan Gao:
Detecting Outliers Using Rule-Based Modeling for Improving CBR-Based Software Quality Classification Models. ICCBR 2003: 216-230 - [c60]Taghi M. Khoshgoftaar, Laurent A. Nguyen, Kehan Gao, Jayanth Rajeevalochanam:
Application of an Attribute Selection Method to CBR-Based Software Quality Classification. ICTAI 2003: 47-52 - [c59]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
Genetic Programming-Based Decision Trees for Software Quality Classification. ICTAI 2003: 374-383 - [c58]Taghi M. Khoshgoftaar, Erik Geleyn, Laurent A. Nguyen:
Empirical Case Studies of Combining Software Quality Classification Models. QSIC 2003: 40- - 2002
- [j47]Taghi M. Khoshgoftaar, Xiaojing Yuan, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Uncertain Classification of Fault-Prone Software Modules. Empir. Softw. Eng. 7(4): 295-295 (2002) - [j46]Taghi M. Khoshgoftaar, Bojan Cukic, Naeem Seliya:
Predicting Fault-Prone Modules in Embedded Systems Using Analogy-Based Classification Models. Int. J. Softw. Eng. Knowl. Eng. 12(2): 201-221 (2002) - [j45]Taghi M. Khoshgoftaar, Edward B. Allen, Jianyu Deng:
Using regression trees to classify fault-prone software modules. IEEE Trans. Reliab. 51(4): 455-462 (2002) - [c57]Ali Idri, Taghi M. Khoshgoftaar, Alain Abran:
Can neural networks be easily interpreted in software cost estimation? FUZZ-IEEE 2002: 1162-1167 - [c56]Taghi M. Khoshgoftaar, Erik Geleyn, Laurent A. Nguyen, Lofton A. Bullard:
Cost-Sensitive Boosting In Software Quality Modeling. HASE 2002: 51-62 - [c55]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Quality Classification Modeling Using The SPRINT Decision Tree Algorithm. ICTAI 2002: 365-374 - [c54]Taghi M. Khoshgoftaar:
Improving Usefulness of Software Quality Classification Models Based on Boolean Discriminant Functions. ISSRE 2002: 221-230 - [c53]Ali Idri, Alain Abran, Taghi M. Khoshgoftaar:
Estimating Software Project Effort by Analogy Based on Linguistic Values. IEEE METRICS 2002: 21- - [c52]Taghi M. Khoshgoftaar, Erik Geleyn, Kehan Gao:
An Empirical Study of the Impact of Count Models Predictions on Module-Order Models. IEEE METRICS 2002: 161-172 - [c51]Taghi M. Khoshgoftaar, Naeem Seliya:
Tree-Based Software Quality Estimation Models For Fault Prediction. IEEE METRICS 2002: 203- - 2001
- [j44]Zhiwei Xu, Taghi M. Khoshgoftaar:
Software Quality Prediction for High-Assurance Network Telecommunications Systems. Comput. J. 44(6): 557-568 (2001) - [j43]Taghi M. Khoshgoftaar, Edward B. Allen:
Controlling Overfitting in Classification-Tree Models of Software Quality. Empir. Softw. Eng. 6(1): 59-79 (2001) - [j42]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Cost-Benefit Analysis of Software Quality Models. Softw. Qual. J. 9(1): 9-30 (2001) - [j41]Taghi M. Khoshgoftaar, Edward B. Allen:
Empirical Assessment of a Software Metric: The Information Content of Operators. Softw. Qual. J. 9(2): 99-112 (2001) - [j40]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Data Mining of Software Development Databases. Softw. Qual. J. 9(3): 161-176 (2001) - [c50]Yi Liu, Taghi M. Khoshgoftaar:
Genetic Programming Model for Software Quality Classification. HASE 2001: 127-138 - [c49]Taghi M. Khoshgoftaar, Kehan Gao, Robert M. Szabo:
An Application of Zero-Inflated Poisson Regression for Software Fault Prediction . ISSRE 2001: 66-73 - [c48]Edward B. Allen, Taghi M. Khoshgoftaar, Ye Chen:
Measuring Coupling and Cohesion of Software Modules: An Information-Theory Approach. IEEE METRICS 2001: 124-134 - [c47]Taghi M. Khoshgoftaar, Edward B. Allen, Jianyu Deng:
Controlling Overfitting in Software Quality Models: Experiments with Regression Trees and Classification. IEEE METRICS 2001: 190-198 - 2000
- [j39]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Accuracy of software quality models over multiple releases. Ann. Softw. Eng. 9: 103-116 (2000) - [j38]Taghi M. Khoshgoftaar, Xiaojing Yuan, Edward B. Allen:
Balancing Misclassification Rates in Classification-Tree Models of Software Quality. Empir. Softw. Eng. 5(4): 313-330 (2000) - [j37]K. Ganesan, Taghi M. Khoshgoftaar, Edward B. Allen:
Case-Based Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. 10(2): 139-152 (2000) - [j36]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Classification-tree models of software-quality over multiple releases. IEEE Trans. Reliab. 49(1): 4-11 (2000) - [j35]Taghi M. Khoshgoftaar, Edward B. Allen:
A practical classification-rule for software-quality models. IEEE Trans. Reliab. 49(2): 209-216 (2000) - [c46]Zhiwei Xu, Taghi M. Khoshgoftaar, Edward B. Allen:
Prediction of software faults using fuzzy nonlinear regression modeling. HASE 2000: 281-290 - [c45]Taghi M. Khoshgoftaar, Ruqun Shan, Edward B. Allen:
Using product, process, and execution metrics to predict fault-prone software modules with classification trees. HASE 2000: 301-310 - [c44]Taghi M. Khoshgoftaar, Edward B. Allen, Jason C. Busboom:
Modeling software quality: the Software Measurement Analysis and Reliability Toolkit. ICTAI 2000: 54-61 - [c43]Taghi M. Khoshgoftaar, Ruqun Shan, Edward B. Allen:
Improving Tree-Based Models of Software Quality with Principal Components Analysis. ISSRE 2000: 198-209 - [c42]Taghi M. Khoshgoftaar, Vishal Thaker, Edward B. Allen:
Modeling Fault-Prone Modules of Subsystems. ISSRE 2000: 259-269
1990 – 1999
- 1999
- [j34]Taghi M. Khoshgoftaar, Edward B. Allen:
A Comparative Study of Ordering and Classification of Fault-Prone Software Modules. Empir. Softw. Eng. 4(2): 159-186 (1999) - [j33]Taghi M. Khoshgoftaar, Edward B. Allen, Archana Naik, Wendell D. Jones, John P. Hudepohl:
Using Classification Trees for Software Quality Models: Lessons Learned. Int. J. Softw. Eng. Knowl. Eng. 9(2): 217-231 (1999) - [j32]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Data Mining for Predictors of Software Quality. Int. J. Softw. Eng. Knowl. Eng. 9(5): 547-563 (1999) - [j31]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Which Software Modules have Faults which will be Discovered by Customers? J. Softw. Maintenance Res. Pract. 11(1): 1-18 (1999) - [c41]Wendell D. Jones, John P. Hudepohl, Taghi M. Khoshgoftaar, Edward B. Allen:
Application of a Usage Profile in Software Quality Models. CSMR 1999: 148-159 - [c40]Matthew P. Evett, Taghi M. Khoshgoftaar, Pei-der Chien, Edward B. Allen:
Using Genetic Programming to Determine Software Quality. FLAIRS 1999: 113-117 - [c39]Matthew P. Evett, Taghi M. Khoshgoftaar, Pei-der Chien, Edward B. Allen:
Modelling software quality with GP. GECCO 1999: 1232 - [c38]Taghi M. Khoshgoftaar, Edward B. Allen:
Predicting Fault-Prone Software Modules in Embedded Systems with Classification Trees. HASE 1999: 105-114 - [c37]Taghi M. Khoshgoftaar, Edward B. Allen, Xiaojing Yuan, Wendell D. Jones, John P. Hudepohl:
Experience Paper: Preparing Measurements of Legacy Software for Predicting Operational Faults. ICSM 1999: 359- - [c36]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Classification tree models of software quality over multiple releases. ISSRE 1999: 116-125 - [c35]Edward B. Allen, Taghi M. Khoshgoftaar:
Measuring Coupling and Cohesion: An Information-Theory Approach. IEEE METRICS 1999: 119- - [c34]Taghi M. Khoshgoftaar, Edward B. Allen, Xiaojing Yuan, Wendell D. Jones, John P. Hudepohl:
Assessing Uncertain Predictions of Software Quality. IEEE METRICS 1999: 159- - [c33]Norman F. Schneidewind, Wendell D. Jones, Taghi M. Khoshgoftaar, Paul W. Oman, George E. Stark:
Can Metrics and Models be Applied Across Multiple Releases or Projects? IEEE METRICS 1999: 324- - 1998
- [j30]Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, Ronald M. Flass:
Using Process History to Predict Software Quality. Computer 31(4): 66-72 (1998) - [j29]Taghi M. Khoshgoftaar, Edward B. Allen:
Classification of Fault-Prone Software Modules: Prior Probabilities, Costs, and Model Evaluation. Empir. Softw. Eng. 3(3): 275-298 (1998) - [c32]Taghi M. Khoshgoftaar, Edward B. Allen, Archana Naik, Wendell D. Jones, John P. Hudepohl:
Using Classification Trees for Software Quality Models: Lessons Learned. HASE 1998: 82-89 - [c31]Donald F. Schenker, Taghi M. Khoshgoftaar:
The Application of Fuzzy Enhanced Case-Based Reasoning for Identifying Fault-Prone Modules. HASE 1998: 90-97 - [c30]Wendell D. Jones, Taghi M. Khoshgoftaar, John C. Munson, T. Troy Pearse, George E. Stark:
Hitting the Moving Target: Trials and Tribulations of Modeling Quality in Evolving Software Systems. ICSM 1998: 66-67 - [c29]Taghi M. Khoshgoftaar, Edward B. Allen:
Can a Software Quality Model Hit a Moving Target? ICSM 1998: 68-70 - [c28]Taghi M. Khoshgoftaar, Edward B. Allen:
Predicting the order of fault-prone modules in legacy software. ISSRE 1998: 344-353 - 1997
- [j28]Taghi M. Khoshgoftaar, Edward B. Allen, David L. Lanning:
An Information Theory-Based Approach to Quantifying the Contribution of a Software Metric. J. Syst. Softw. 36(2): 103-113 (1997) - [j27]Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl, Stephen J. Aud:
Application of neural networks to software quality modeling of a very large telecommunications system. IEEE Trans. Neural Networks 8(4): 902-909 (1997) - [c27]Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, Ronald M. Flass:
Process Measures for Predicting Software Quality. HASE 1997: 155-161 - [c26]Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl:
Evolutionary neural networks: a robust approach to software reliability problems. ISSRE 1997: 13-26 - [c25]Taghi M. Khoshgoftaar, K. Ganesan, Edward B. Allen, Fletcher D. Ross, Rama Munikoti, Nishith Goel, Amit Nandi:
Predicting fault-prone modules with case-based reasoning. ISSRE 1997: 27-35 - [c24]Taghi M. Khoshgoftaar, Edward B. Allen:
The Impact of Costs of Misclassification on Software Quality Modeling. IEEE METRICS 1997: 54- - 1996
- [j26]Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel:
The impact of software evolution and reuse on software quality. Empir. Softw. Eng. 1(1): 31-44 (1996) - [j25]Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel:
Early Quality Prediction: A Case Study in Telecommunications. IEEE Softw. 13(1): 65-71 (1996) - [j24]John P. Hudepohl, Stephen J. Aud, Taghi M. Khoshgoftaar, Edward B. Allen, Jean Mayrand:
Emerald: Software Metrics and Models on the Desktop. IEEE Softw. 13(5): 56-60 (1996) - [j23]Taghi M. Khoshgoftaar, David L. Lanning:
Analysis and differentiation of software system environments. Softw. Qual. J. 5(2): 127-139 (1996) - [j22]Taghi M. Khoshgoftaar, Robert M. Szabo:
Using neural networks to predict software faults during testing. IEEE Trans. Reliab. 45(3): 456-462 (1996) - [c23]Taghi M. Khoshgoftaar, Edward B. Allen, Lofton A. Bullard, Robert Halstead, Gary P. Trio:
A tree-based classification model for analysis of a military software system. HASE 1996: 244-251 - [c22]Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio:
Detection of Fault-Prone Software Modules During a Spiral Life Cycle. ICSM 1996: 69-76 - [c21]John P. Hudepohl, Stephen J. Aud, Taghi M. Khoshgoftaar, Edward B. Allen, Jean Mayrand:
Integrating metrics and models for software risk assessment. ISSRE 1996: 93-98 - [c20]Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl:
Using the genetic algorithm to build optimal neural networks for fault-prone module detection. ISSRE 1996: 152-162 - [c19]Taghi M. Khoshgoftaar, Edward B. Allen, Nishith Goel, Amit Nandi, John McMullan:
Detection of software modules with high debug code churn in a very large legacy system. ISSRE 1996: 364-371 - 1995
- [j21]Taghi M. Khoshgoftaar, Abhijit S. Pandya, David L. Lanning:
Application of Neural Networks for Predicting Program Faults. Ann. Softw. Eng. 1: 141-154 (1995) - [j20]Taghi M. Khoshgoftaar, Robert M. Szabo, Peter J. Guasti:
Exploring the behaviour of neural network software quality models. Softw. Eng. J. 10(3): 89-96 (1995) - [j19]Taghi M. Khoshgoftaar, David L. Lanning:
A neural network approach for early detection of program modules having high risk in the maintenance phase. J. Syst. Softw. 29(1): 85-91 (1995) - [j18]Khoa D. Huynh, Taghi M. Khoshgoftaar:
A Performance Analysis of an Object-Based I/O Architecture in a Video Server Environment. Multim. Syst. 3(4): 162-177 (1995) - [j17]Khoa D. Huynh, Taghi M. Khoshgoftaar:
Performance Analysis of a Peer-to-Peer I/O Architecture in Video Server Environments. Multim. Tools Appl. 1(3): 217-244 (1995) - [j16]Taghi M. Khoshgoftaar, Robert M. Szabo:
Investigating ARIMA models of software system quality. Softw. Qual. J. 4(1): 33-48 (1995) - [c18]Taghi M. Khoshgoftaar, Edward B. Allen:
Multivariate assessment of complex software systems: a comparative study. ICECCS 1995: 389-396 - [c17]Taghi M. Khoshgoftaar, Robert M. Szabo, Jeffrey M. Voas:
Detecting program modules with low testability. ICSM 1995: 242-250 - [c16]Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel, John P. Hudepohl, Jean Mayrand:
Detection of fault-prone program modules in a very large telecommunications system. ISSRE 1995: 24-33 - [c15]Robert M. Szabo, Taghi M. Khoshgoftaar:
An assessment of software quality in a C++ environment. ISSRE 1995: 240-249 - 1994
- [j15]Taghi M. Khoshgoftaar, Paul W. Oman:
Software Metrics: Charting the Course - Guest Editors' Introduction. Computer 27(9): 13-15 (1994) - [j14]David L. Lanning, Taghi M. Khoshgoftaar:
Modeling the Relationship Between Source Code Complexity and Maintenance Difficulty. Computer 27(9): 35-40 (1994) - [j13]Khoa D. Huynh, Taghi M. Khoshgoftaar:
A performance analysis of advanced I/O architectures for PC-based network file servers. Distributed Syst. Eng. 1(6): 332- (1994) - [j12]Taghi M. Khoshgoftaar, David L. Lanning, Abhijit S. Pandya:
A comparative study of pattern recognition techniques for quality evaluation of telecommunications software. IEEE J. Sel. Areas Commun. 12(2): 279-291 (1994) - [j11]Taghi M. Khoshgoftaar, John C. Munson, David L. Lanning:
Alternative approaches for the use of metrics to order programs by complexity. J. Syst. Softw. 24(3): 211-221 (1994) - [j10]Khoa D. Huynh, Taghi M. Khoshgoftaar:
Performance Analysis of Advanced I/O Architectures for PC-Based Video Servers. Multim. Syst. 2(1): 36-50 (1994) - [j9]Khoa D. Huynh, Taghi M. Khoshgoftaar:
A Performance Analysis of Personal Computers in a Video Conferencing Environment. Multim. Syst. 2(3): 103-117 (1994) - [c14]Taghi M. Khoshgoftaar, Robert M. Szabo:
Improving Code Churn Predictions During the System Test and Maintenance Phases. ICSM 1994: 58-67 - [c13]David L. Lanning, Taghi M. Khoshgoftaar:
Canonical Modeling of Software Complexity and Fault Correction Activity. ICSM 1994: 374-381 - [c12]Taghi M. Khoshgoftaar, David L. Lanning:
On the impact of software product dissimilarity on software quality models. ISSRE 1994: 104-114 - [c11]Taghi M. Khoshgoftaar, David L. Lanning:
Are the principal components of software complexity data stable across software products? IEEE METRICS 1994: 61-72 - 1993
- [j8]Khoa D. Huynh, Taghi M. Khoshgoftaar, Gerald Marazas:
A high-level performance analysis of the IBM subsystem control block (SCB) architecture. Microprocess. Microprogramming 36(3): 109-125 (1993) - [j7]John C. Munson, Taghi M. Khoshgoftaar:
Measurement of data structure complexity. J. Syst. Softw. 20(3): 217-225 (1993) - [c10]Taghi M. Khoshgoftaar, John C. Munson, David L. Lanning:
A Comparative Study of Predictive Models for Program Changes During System Testing and Maintenance. ICSM 1993: 72-79 - [c9]Taghi M. Khoshgoftaar, David L. Lanning, Abhijit S. Pandya:
A neural network modeling methodology for the detection of high-risk programs. ISSRE 1993: 302-309 - [c8]Taghi M. Khoshgoftaar, John C. Munson, David L. Lanning:
Dynamic system complexity. IEEE METRICS 1993: 129-140 - [c7]Khoa D. Huynh, Taghi M. Khoshgoftaar:
A Performance Analysis of the IBM Subsystem Control Block Architecture in a Video Conferencing Environment. ACM Multimedia 1993: 321-330 - 1992
- [j6]Khoa D. Huynh, Eduardo B. Fernández, Taghi M. Khoshgoftaar:
A workload model for frame-based real-time applications on distributed systems. J. Syst. Softw. 18(3): 255-271 (1992) - [j5]John C. Munson, Taghi M. Khoshgoftaar:
Measuring Dynamic Program Complexity. IEEE Softw. 9(6): 48-55 (1992) - [j4]John C. Munson, Taghi M. Khoshgoftaar:
The Detection of Fault-Prone Programs. IEEE Trans. Software Eng. 18(5): 423-433 (1992) - [j3]Taghi M. Khoshgoftaar, John C. Munson, Bibhuti B. Bhattacharya, Gary D. Richardson:
Predictive Modeling Techniques of Software Quality from Software Measures. IEEE Trans. Software Eng. 18(11): 979-987 (1992) - [c6]John C. Munson, Taghi M. Khoshgoftaar:
Software measurement for the space shuttle HAL/S maintenance environment. ICSM 1992 - [c5]Taghi M. Khoshgoftaar, Abhijit S. Pandya, Hemant B. More:
A neural network approach for predicting software development faults. ISSRE 1992: 83-89 - 1991
- [c4]John C. Munson, Taghi M. Khoshgoftaar:
The use of software complexity metrics in software reliability modeling. ISSRE 1991: 2-11 - [c3]Taghi M. Khoshgoftaar, Timothy G. Woodcock:
Software reliability model selection: a cast study. ISSRE 1991: 183-191 - 1990
- [j2]Taghi M. Khoshgoftaar, John C. Munson:
Predicting Software Development Errors Using Software Complexity Metrics. IEEE J. Sel. Areas Commun. 8(2): 253-261 (1990) - [j1]John C. Munson, Taghi M. Khoshgoftaar:
Applications of a relative complexity metric for software project management. J. Syst. Softw. 12(3): 283-291 (1990) - [c2]Taghi M. Khoshgoftaar, John C. Munson:
The lines of code metric as a predictor of program faults: a critical analysis. COMPSAC 1990: 408-413
1980 – 1989
- 1989
- [c1]John C. Munson, Taghi M. Khoshgoftaar:
The Dimensionality of Program Complexity. ICSE 1989: 245-253
Coauthor Index
[j50] [j49] [j47] [j45] [j43] [j42] [j41] [j40] [c48] [c47] [j39] [j38] [j37] [j36] [j35] [c46] [c45] [c44] [c43] [c42] [j34] [j33] [j32] [j31] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [j30] [j29] [c32] [c29] [c28] [j28] [j27] [c27] [c26] [c25] [c24] [j26] [j25] [j24] [c23] [c22] [c21] [c20] [c19] [c18] [c16]
[j139] [j137] [c261] [j132] [j131] [j130] [j127] [c251] [c245] [c242] [c238] [c211] [j119] [j117] [j114] [c199] [c191] [c185] [j112] [j109] [c171] [c163] [c161] [c160] [c159] [c153] [c151] [c149] [c148] [c147] [c144] [c143] [c140] [c138] [c135] [r1] [j79] [j78] [c107] [j72] [c91] [j66] [j64] [j63] [j60] [c78] [c68] [c64] [c61] [c60] [c52] [c49]
[j129] [j128] [j120] [j116] [c193] [j113] [j112] [j110] [j108] [c181] [c169] [c166] [j104] [j102] [j100] [c159] [c158] [c157] [c156] [c154] [c152] [c149] [j98] [j97] [j95] [j92] [j90] [c145] [c141] [c139] [c137] [c136] [j86] [j85] [c131] [c129] [c127] [c125] [c122] [c121] [c119] [c118] [j84] [j81] [c110] [c108] [c106] [c104] [c103] [c100] [c98] [c95] [j74] [j73] [c93] [c90] [c86] [c85] [c84] [c83] [j61] [c81] [c76]
[c329] [c298] [c294] [c284] [p3] [j140] [j139] [j137] [c280] [c277] [c276] [c275] [c274] [c273] [c270] [c269] [c265] [c263] [c262] [c261] [j132] [j127] [c259] [c258] [c257] [c254] [c253] [c252] [c250] [c249] [c247] [c246] [c245] [c242] [c241] [c240] [c238] [c237] [c236] [c235] [c234] [c233] [c232] [c229] [c228] [c226] [c225] [c224] [c222] [c221] [c220] [c217] [c215] [c214] [c212] [c211] [j121] [j118] [j117] [j116] [c209] [c205] [c202] [c200] [c199] [c198] [c197] [c195] [c193] [c192] [c191] [c190] [c187] [c186] [c185] [c184] [j113] [j110] [j108] [c182] [c171] [c168] [c166] [c164] [c163] [c162] [j104] [j102] [j100] [c160] [c156] [c155] [c152] [j95] [c145] [c142] [c140] [c137] [c129] [c127] [c126] [c125] [c122] [c121] [c110] [c108] [c106] [c104]
Gabriel Castaneda Oscos
aka: Gabriel Castaneda
aka: Gabriel Castaneda
[j180] [c374] [j173] [j158] [c347] [c339] [e4] [j149] [c296] [p4] [j138] [j136] [c282] [c279] [j131] [c255] [j114] [j109] [j106] [c179] [j105] [j101] [c157] [c153] [c150] [j94] [j91] [c144] [c141] [c138] [c136] [c133] [j87] [c120] [c114] [p2] [p1] [j80] [j76] [c111] [j70] [j69] [j68] [c89] [j66] [j63] [j59] [c82] [c80] [c77] [j58] [j57] [j56] [j54] [c73] [c71] [c68] [c66] [c65] [j53] [j52] [j51] [c59] [j46] [c55] [c51]
[p4] [j136] [j135] [j127] [j126] [c260] [c259] [c257] [c252] [c251] [c250] [c249] [c248] [c244] [c243] [j125] [c240] [c239] [c236] [c235] [c234] [c233] [c232] [c231] [c230] [c229] [c228] [c227] [c226] [c225] [c224] [c223] [c222] [c221] [c220] [c219] [c218] [c217] [c216] [c215] [c214] [c213] [c212] [j120] [j116] [c209] [c208] [c207] [c206] [c205] [c204] [c203] [c202] [c201] [c198] [c196] [c195] [c192] [c190] [c188] [c187] [c186] [c184] [c183] [c182] [c177] [c174] [c173] [c169] [c167] [c165] [c154] [c145]
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