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ORCIDJohn T. Hancock
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- affiliation: Florida Atlantic University, Boca Raton, FL, USA
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2020 – today
- 2024
[j16]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)- [j15]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) - 2023
- [j14]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Evaluating classifier performance with highly imbalanced Big Data. J. Big Data 10(1): 42 (2023) - [j13]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) - [j12]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) - [j11]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) - [j10]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) - [j9]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) - [c24]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 - [c23]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 - [c22]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 - [c21]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 - [c20]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 - [c19]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud. IRI 2023: 267-272 - [c18]Huanjing Wang, John T. Hancock, Taghi M. Khoshgoftaar:
Improving Medicare Fraud Detection through Big Data Size Reduction Techniques. SOSE 2023: 208-217 - 2022
- [j8]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) - [j7]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) - [j6]John T. Hancock, Taghi M. Khoshgoftaar:
Hyperparameter Tuning for Medicare Fraud Detection in Big Data. SN Comput. Sci. 3(6): 440 (2022) - [c17]John T. Hancock, Justin M. Johnson, Taghi M. Khoshgoftaar:
A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data. CIC 2022: 135-142 - [c16]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. ICMLA 2022: 1419-1426 - [c15]Joffrey L. Leevy, Taghi M. Khoshgoftaar, John T. Hancock:
Evaluating Performance Metrics for Credit Card Fraud Classification. ICTAI 2022: 1336-1341 - [c14]John T. Hancock, Taghi M. Khoshgoftaar:
Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection. IRI 2022: 243-249 - [c13]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
- [j5]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) - [j4]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting web attacks using random undersampling and ensemble learners. J. Big Data 8(1): 75 (2021) - [j3]John T. Hancock, Taghi M. Khoshgoftaar:
Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection. SN Comput. Sci. 2(4): 268 (2021) - [c12]John T. Hancock, Taghi M. Khoshgoftaar:
Leveraging LightGBM for Categorical Big Data. BigDataService 2021: 149-154 - [c11]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 - [c10]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 - [c9]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. CSR 2021: 27-34 - [c8]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers. ICMLA 2021: 30-37 - [c7]John T. Hancock, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
Detecting SSH and FTP Brute Force Attacks in Big Data. ICMLA 2021: 760-765 - [c6]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 - [c5]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting Web Attacks in Severely Imbalanced Network Traffic Data. IRI 2021: 267-273 - [c4]John T. Hancock, Taghi M. Khoshgoftaar:
Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. IRI 2021: 348-354 - 2020
- [j2]John T. Hancock, Taghi M. Khoshgoftaar:
Survey on categorical data for neural networks. J. Big Data 7(1): 28 (2020) - [j1]John T. Hancock, Taghi M. Khoshgoftaar:
CatBoost for big data: an interdisciplinary review. J. Big Data 7(1): 94 (2020) - [c3]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 - [c2]John T. Hancock, Taghi M. Khoshgoftaar:
Performance of CatBoost and XGBoost in Medicare Fraud Detection. ICMLA 2020: 572-579 - [c1]John T. Hancock, Taghi M. Khoshgoftaar:
Medicare Fraud Detection using CatBoost. IRI 2020: 97-103
Coauthor Index
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