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- affiliation: KU Leuven, Faculty of Business and Economics, Belgium
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2020 – today
- 2024
[j130]Bart Baesens
, Amy Adams, Rodrigo Pacheco-Ruiz, Ann-Sophie Baesens, Seppe K. L. M. vanden Broucke:
Explainable Deep Learning to Classify Royal Navy Ships. IEEE Access 12: 1774-1785 (2024)- [j129]Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir, Bart Baesens, Monique Snoeck, Cristián Bravo:
INFLECT-DGNN: Influencer Prediction With Dynamic Graph Neural Networks. IEEE Access 12: 115026-115041 (2024) - [j128]Boje Deforce, Bart Baesens, Jan Diels, Estefanía Serral Asensio:
Harnessing the power of transformers and data fusion in smart irrigation. Appl. Soft Comput. 152: 111246 (2024) - [j127]Robin Van Oirbeek, Jolien Ponnet, Bart Baesens, Tim Verdonck:
Computational Efficient Approximations of the Concordance Probability in a Big Data Setting. Big Data 12(3): 243-268 (2024) - [j126]Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke:
A new perspective on classification: Optimally allocating limited resources to uncertain tasks. Decis. Support Syst. 179: 114151 (2024) - [j125]Elena Tiukhova, Pavani Vemuri, Nidia Guadalupe López Flores, Anna Sigridur Islind, María Óskarsdóttir, Stephan Poelmans, Bart Baesens, Monique Snoeck:
Explainable Learning Analytics: Assessing the stability of student success prediction models by means of explainable AI. Decis. Support Syst. 182: 114229 (2024) - [j124]Koen W. De Bock, Kristof Coussement, Arno De Caigny, Roman Slowinski, Bart Baesens, Robert N. Boute, Tsan-Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke, Richard Weber:
Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. Eur. J. Oper. Res. 317(2): 249-272 (2024) - [j123]Manon Reusens, Alexander Stevens, Jonathan Tonglet, Johannes De Smedt, Wouter Verbeke, Seppe vanden Broucke, Bart Baesens:
Evaluating text classification: A benchmark study. Expert Syst. Appl. 254: 124302 (2024) - [j122]Tim Verdonck, Bart Baesens, María Óskarsdóttir, Seppe vanden Broucke:
Special issue on feature engineering editorial. Mach. Learn. 113(7): 3917-3928 (2024) - [j121]Wouter Dossche, Sarah Vansteenkiste, Bart Baesens, Wilfried Lemahieu:
Interpretable and Accurate Identification of Job Seekers at Risk of Long-Term Unemployment: Explainable ML-Based Profiling. SN Comput. Sci. 5(5): 536 (2024) - [c72]Elena Tiukhova, Pavani Vemuri, Maria Óskarsdóttir, Stephan Poelmans, Bart Baesens, Monique Snoeck:
Discovering Unusual Study Patterns Using Anomaly Detection and XAI. HICSS 2024: 1427-1436 - [i22]Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu:
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection. CoRR abs/2404.02865 (2024) - [i21]Boje Deforce, Bart Baesens, Estefanía Serral Asensio:
Leveraging Time-Series Foundation Models in Smart Agriculture for Soil Moisture Forecasting. CoRR abs/2405.18913 (2024) - [i20]Bruno Deprez, Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke:
Network Analytics for Anti-Money Laundering - A Systematic Literature Review and Experimental Evaluation. CoRR abs/2405.19383 (2024) - [i19]Manon Reusens, Philipp Borchert, Jochen De Weerdt, Bart Baesens:
Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance. CoRR abs/2406.17385 (2024) - 2023
- [j120]Rafaël Van Belle, Bart Baesens, Jochen De Weerdt:
CATCHM: A novel network-based credit card fraud detection method using node representation learning. Decis. Support Syst. 164: 113866 (2023) - [c71]Boje Deforce, Bart Baesens, Jan Diels, Estefanía Serral Asensio:
MultiMix TFT: A Multi-task Mixed-Frequency Framework with Temporal Fusion Transformers. CoLLAs 2023: 586-600 - [c70]Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt, Bart Baesens:
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. EMNLP 2023: 2887-2896 - [c69]Jonathan Tonglet, Manon Reusens, Philipp Borchert, Bart Baesens:
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA. EMNLP 2023: 13569-13583 - [c68]Elena Tiukhova, Charlotte Verbruggen, Bart Baesens, Monique Snoeck:
Learning analytics tells: Know your basics and go to class. ER (Companion) 2023 - [i18]Boje Deforce, Bart Baesens, Jan Diels, Estefanía Serral Asensio:
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors. CoRR abs/2305.05495 (2023) - [i17]Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir, Bart Baesens, Monique Snoeck, Cristián Bravo:
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks. CoRR abs/2307.08131 (2023) - [i16]Jonathan Tonglet, Manon Reusens, Philipp Borchert, Bart Baesens:
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA. CoRR abs/2310.06675 (2023) - [i15]Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt, Bart Baesens:
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. CoRR abs/2310.10310 (2023) - 2022
- [j119]Sebastiaan Höppner, Bart Baesens, Wouter Verbeke, Tim Verdonck:
Instance-dependent cost-sensitive learning for detecting transfer fraud. Eur. J. Oper. Res. 297(1): 291-300 (2022) - [j118]Toon Vanderschueren, Tim Verdonck, Bart Baesens, Wouter Verbeke:
Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies. Inf. Sci. 594: 400-415 (2022) - [c67]Simon Hiel, Lore Nicolaers, Carlos Ortega Vázquez, Sandra Mitrovic, Bart Baesens, Jochen De Weerdt:
Evaluation of Joint Modeling Techniques for Node Embedding and Community Detection on Graphs. ASONAM 2022: 403-410 - [c66]Toon Vanderschueren, Wouter Verbeke, Bart Baesens, Tim Verdonck:
Instance-dependent cost-sensitive learning: do we really need it? HICSS 2022: 1-9 - [c65]Tim Verdonck, Wouter Verbeke, Maria Óskarsdóttir, Bart Baesens:
Introduction to the Minitrack on Fraud Detection Using Machine Learning. HICSS 2022: 1-2 - [c64]Elena Tiukhova, Manon Reusens, Bart Baesens, Monique Snoeck:
Benchmarking Conventional Outlier Detection Methods. RCIS 2022: 597-613 - [i14]Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke:
A new perspective on classification: optimally allocating limited resources to uncertain tasks. CoRR abs/2202.04369 (2022) - [i13]Toon Vanderschueren, Robert N. Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke:
Prescriptive maintenance with causal machine learning. CoRR abs/2206.01562 (2022) - [i12]Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir, Hernán García, Alejandro Correa Bahnsen, Bart Baesens, Monique Snoeck, Cristián Bravo:
Influencer Detection with Dynamic Graph Neural Networks. CoRR abs/2211.09664 (2022) - 2021
- [j117]Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke:
Autoencoders for strategic decision support. Decis. Support Syst. 150: 113422 (2021) - [j116]Bart Baesens, Sebastiaan Höppner, Tim Verdonck:
Data engineering for fraud detection. Decis. Support Syst. 150: 113492 (2021) - [j115]Björn Rafn Gunnarsson, Seppe vanden Broucke, Bart Baesens, María Óskarsdóttir, Wilfried Lemahieu:
Deep learning for credit scoring: Do or don't? Eur. J. Oper. Res. 295(1): 292-305 (2021) - [j114]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
tcc2vec: RFM-informed representation learning on call graphs for churn prediction. Inf. Sci. 557: 270-285 (2021) - [j113]Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt:
Expert-driven trace clustering with instance-level constraints. Knowl. Inf. Syst. 63(5): 1197-1220 (2021) - [i11]Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt:
Expert-driven Trace Clustering with Instance-level Constraints. CoRR abs/2110.06703 (2021) - 2020
- [j112]Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck:
Profit driven decision trees for churn prediction. Eur. J. Oper. Res. 284(3): 920-933 (2020) - [j111]Laura Calzada-Infante, María Óskarsdóttir, Bart Baesens:
Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts. Expert Syst. Appl. 160: 113553 (2020) - [i10]María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry. CoRR abs/2001.06700 (2020) - [i9]María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture. CoRR abs/2001.06701 (2020) - [i8]María Óskarsdóttir, Cristián Bravo, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending. CoRR abs/2001.10994 (2020) - [i7]Tine Van Calster, Filip Van den Bossche, Bart Baesens, Wilfried Lemahieu:
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective. CoRR abs/2002.00949 (2020) - [i6]María Óskarsdóttir, Cristián Bravo, Carlos Sarraute, Jan Vanthienen, Bart Baesens:
The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics. CoRR abs/2002.09931 (2020) - [i5]Bart Baesens, Sebastiaan Höppner, Irene Ortner, Tim Verdonck:
robROSE: A robust approach for dealing with imbalanced data in fraud detection. CoRR abs/2003.11915 (2020) - [i4]Sam Verboven, Jeroen Berrevoets, Chris Wuytens, Bart Baesens, Wouter Verbeke:
Autoencoders for strategic decision support. CoRR abs/2005.01075 (2020) - [i3]María Óskarsdóttir, Waqas Ahmed, Katrien Antonio, Bart Baesens, Rémi Dendievel, Tom Donas, Tom Reynkens:
Social network analytics for supervised fraud detection in insurance. CoRR abs/2009.08313 (2020)
2010 – 2019
- 2019
- [j110]María Óskarsdóttir, Cristián Bravo, Carlos Sarraute, Jan Vanthienen, Bart Baesens:
The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Appl. Soft Comput. 74: 26-39 (2019) - [j109]Bart Baesens:
BART: BAckward Regression Trimming. Big Data 7(3): 207-213 (2019) - [j108]Jasmien Lismont, Tine Van Calster, María Óskarsdóttir, Seppe vanden Broucke, Bart Baesens, Wilfried Lemahieu, Jan Vanthienen:
Closing the Gap Between Experts and Novices Using Analytics-as-a-Service: An Experimental Study. Bus. Inf. Syst. Eng. 61(6): 679-693 (2019) - [j107]Nikita Kozodoi, Stefan Lessmann, Konstantinos Papakonstantinou, Yiannis Gatsoulis, Bart Baesens:
A multi-objective approach for profit-driven feature selection in credit scoring. Decis. Support Syst. 120: 106-117 (2019) - [c63]María Óskarsdóttir, Sander Cornette, Floris Deseure, Bart Baesens:
Inductive Representation Learning on Feature Rich Complex Networks for Churn Prediction in Telco. COMPLEX NETWORKS (1) 2019: 845-853 - [i2]Libo Li, Stefan Lessmann, Bart Baesens:
Evaluating software defect prediction performance: an updated benchmarking study. CoRR abs/1901.01726 (2019) - 2018
- [j106]Bart Baesens, Wouter Verbeke, Cristián Bravo:
Special Issue on Profit-Driven Analytics. Big Data 6(1): 1-2 (2018) - [j105]María Óskarsdóttir, Bart Baesens, Jan Vanthienen:
Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values. Big Data 6(1): 53-65 (2018) - [j104]Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens:
An Interview with Bart Baesens, One of the Authors of Principles of Database Management. Big Data 6(2): 69-71 (2018) - [j103]Jasmien Lismont, Eddy Cardinaels, Liesbeth Bruynseels, Sander De Groote, Bart Baesens, Wilfried Lemahieu, Jan Vanthienen:
Predicting tax avoidance by means of social network analytics. Decis. Support Syst. 108: 13-24 (2018) - [j102]Eugen Stripling, Bart Baesens, Barak Chizi, Seppe vanden Broucke:
Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers' compensation fraud. Decis. Support Syst. 111: 13-26 (2018) - [j101]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
On the operational efficiency of different feature types for telco Churn prediction. Eur. J. Oper. Res. 267(3): 1141-1155 (2018) - [j100]Jasmien Lismont, Sudha Ram, Jan Vanthienen, Wilfried Lemahieu, Bart Baesens:
Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation. Expert Syst. Appl. 104: 22-32 (2018) - [j99]María Óskarsdóttir, Tine Van Calster, Bart Baesens, Wilfried Lemahieu, Jan Vanthienen:
Time series for early churn detection: Using similarity based classification for dynamic networks. Expert Syst. Appl. 106: 55-65 (2018) - [j98]Bing Zhu, Bart Baesens, Aimée Backiel, Seppe K. L. M. vanden Broucke:
Benchmarking sampling techniques for imbalance learning in churn prediction. J. Oper. Res. Soc. 69(1): 49-65 (2018) - [j97]Michael Reusens, Wilfried Lemahieu, Bart Baesens, Luc Sels:
Evaluating recommendation and search in the labor market. Knowl. Based Syst. 152: 62-69 (2018) - [j96]Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck:
Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm Evol. Comput. 40: 116-130 (2018) - [j95]Klaas Nelissen, Monique Snoeck, Seppe K. L. M. vanden Broucke, Bart Baesens:
Swipe and Tell: Using Implicit Feedback to Predict User Engagement on Tablets. ACM Trans. Inf. Syst. 36(4): 35:1-35:36 (2018) - [c62]Sam De Winter, Tim Decuypere, Sandra Mitrovic, Bart Baesens, Jochen De Weerdt:
Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction. ASONAM 2018: 1234-1241 - [c61]Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav S. Sukhatme:
Profit Maximizing Logistic Regression Modeling for Credit Scoring. DSW 2018: 125-129 - [c60]María Óskarsdóttir, Cristián Bravo, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending. ICIS 2018 - [c59]Nikita Kozodoi, Stefan Lessmann, Bart Baesens, Konstantinos Papakonstantinou:
Profit-Oriented Feature Selection in Credit Scoring Applications. OR 2018: 59-65 - 2017
- [j94]Tine Van Calster, Bart Baesens, Wilfried Lemahieu:
ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Appl. Soft Comput. 60: 775-785 (2017) - [j93]Wouter Verbeke, David Martens, Bart Baesens:
RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. Appl. Soft Comput. 60: 858-873 (2017) - [j92]Libo Li, Frank Goethals, Bart Baesens, Monique Snoeck:
Predicting software revision outcomes on GitHub using structural holes theory. Comput. Networks 114: 114-124 (2017) - [j91]Michael Reusens, Wilfried Lemahieu, Bart Baesens, Luc Sels:
A note on explicit versus implicit information for job recommendation. Decis. Support Syst. 98: 26-35 (2017) - [j90]Jan Mendling, Bart Baesens, Abraham Bernstein, Michael Fellmann:
Challenges of smart business process management: An introduction to the special issue. Decis. Support Syst. 100: 1-5 (2017) - [j89]María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
Social network analytics for churn prediction in telco: Model building, evaluation and network architecture. Expert Syst. Appl. 85: 204-220 (2017) - [j88]Jasmien Lismont, Jan Vanthienen, Bart Baesens, Wilfried Lemahieu:
Defining analytics maturity indicators: A survey approach. Int. J. Inf. Manag. 37(3): 114-124 (2017) - [j87]Bing Zhu, Bart Baesens, Seppe K. L. M. vanden Broucke:
An empirical comparison of techniques for the class imbalance problem in churn prediction. Inf. Sci. 408: 84-99 (2017) - [j86]Lore Dirick, Gerda Claeskens, Bart Baesens:
Time to default in credit scoring using survival analysis: a benchmark study. J. Oper. Res. Soc. 68(6): 652-665 (2017) - [j85]Véronique Van Vlasselaer, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens:
GOTCHA! Network-Based Fraud Detection for Social Security Fraud. Manag. Sci. 63(9): 3090-3110 (2017) - [j84]Bing Zhu, Yongge Niu, Jin Xiao, Bart Baesens:
A new transferred feature selection algorithm for customer identification. Neural Comput. Appl. 28(9): 2593-2603 (2017) - [c58]Pieter De Koninck, Klaas Nelissen, Bart Baesens, Seppe vanden Broucke, Monique Snoeck, Jochen De Weerdt:
An Approach for Incorporating Expert Knowledge in Trace Clustering. CAiSE 2017: 561-576 - [c57]Sandra Mitrovic, Gaurav Singh, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
Scalable RFM-enriched Representation Learning for Churn Prediction. DSAA 2017: 79-88 - [c56]Tom Haegemans, Michael Reusens, Bart Baesens, Wilfried Lemahieu, Monique Snoeck:
Towards a Visual Approach to Aggregate Data Quality Measurements. ICIQ 2017 - [c55]Bing Zhu, Seppe vanden Broucke, Bart Baesens, Sebastián Maldonado:
Improving Resampling-based Ensemble in Churn Prediction. LIDTA@PKDD/ECML 2017: 79-91 - [c54]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
Churn Prediction Using Dynamic RFM-Augmented Node2vec. PAP@PKDD/ECML 2017: 122-138 - [i1]Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck:
Profit Driven Decision Trees for Churn Prediction. CoRR abs/1712.08101 (2017) - 2016
- [j83]Xinwei Zhu, Seppe vanden Broucke, Guobin Zhu, Jan Vanthienen, Bart Baesens:
Enabling flexible location-aware business process modeling and execution. Decis. Support Syst. 83: 1-9 (2016) - [j82]Helen-Tadesse Moges, Véronique Van Vlasselaer, Wilfried Lemahieu, Bart Baesens:
Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes - An exploratory study. Decis. Support Syst. 83: 32-46 (2016) - [j81]Seppe K. L. M. vanden Broucke, Filip Caron, Jasmien Lismont, Jan Vanthienen, Bart Baesens:
On the gap between reality and registration: a business event analysis classification framework. Inf. Technol. Manag. 17(4): 393-410 (2016) - [j80]Aimée Backiel, Bart Baesens, Gerda Claeskens:
Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. J. Oper. Res. Soc. 67(9) (2016) - [j79]Bart Baesens, Ravi Bapna, James R. Marsden, Jan Vanthienen, J. Leon Zhao:
Transformational Issues of Big Data and Analytics in Networked Business. MIS Q. 40(4): 807-818 (2016) - [c53]María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan Vanthienen:
A comparative study of social network classifiers for predicting churn in the telecommunication industry. ASONAM 2016: 1151-1158 - 2015
- [j78]Sebastián Maldonado, Álvaro Flores, Thomas Verbraken, Bart Baesens, Richard Weber:
Profit-based feature selection using support vector machines - General framework and an application for customer retention. Appl. Soft Comput. 35: 740-748 (2015) - [j77]Bart Minnaert, David Martens, Manu De Backer, Bart Baesens:
To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms. Data Min. Knowl. Discov. 29(1): 237-272 (2015) - [j76]Véronique Van Vlasselaer, Cristián Bravo, Olivier Caelen, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens:
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75: 38-48 (2015) - [j75]Lore Dirick, Gerda Claeskens, Bart Baesens:
An Akaike information criterion for multiple event mixture cure models. Eur. J. Oper. Res. 241(2): 449-457 (2015) - [j74]Stefan Lessmann, Bart Baesens, Hsin-Vonn Seow, Lyn C. Thomas:
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. Eur. J. Oper. Res. 247(1): 124-136 (2015) - [j73]Alex Seret, Sebastián Maldonado, Bart Baesens:
Identifying next relevant variables for segmentation by using feature selection approaches. Expert Syst. Appl. 42(15-16): 6255-6266 (2015) - [j72]Alex Seret, Andreea Bejinaru, Bart Baesens:
Domain knowledge based segmentation of online banking customers. Intell. Data Anal. 19(s1): S163-S184 (2015) - [j71]Julie Moeyersoms, Enric Junqué de Fortuny, Karel Dejaeger, Bart Baesens, David Martens:
Comprehensible software fault and effort prediction: A data mining approach. J. Syst. Softw. 100: 80-90 (2015) - [c52]Aimée Backiel, Yannick Verbinnen, Bart Baesens, Gerda Claeskens:
Combining Local and Social Network Classifiers to Improve Churn Prediction. ASONAM 2015: 651-658 - [c51]Véronique Van Vlasselaer, Tina Eliassi-Rad, Leman Akoglu, Monique Snoeck, Bart Baesens:
AFRAID: Fraud Detection via Active Inference in Time-evolving Social Networks. ASONAM 2015: 659-666 - [c50]Carlos André R. Pinheiro, Véronique Van Vlasselaer, Bart Baesens, Alexandre G. Evsukoff, Moacyr A. H. B. da Silva, Nelson F. F. Ebecken:
A Models Comparison to Estimate Commuting Trips Based on Mobile Phone Data. CSOC (2) 2015: 35-44 - [c49]Eugen Stripling, Seppe vanden Broucke, Katrien Antonio, Bart Baesens, Monique Snoeck:
Profit maximizing logistic regression modeling for customer churn prediction. DSAA 2015: 1-10 - [c48]Véronique Van Vlasselaer, Leman Akoglu, Tina Eliassi-Rad, Monique Snoeck, Bart Baesens:
Guilt-by-Constellation: Fraud Detection by Suspicious Clique Memberships. HICSS 2015: 918-927 - 2014
- [j70]Wouter Verbeke, David Martens, Bart Baesens:
Social network analysis for customer churn prediction. Appl. Soft Comput. 14: 431-446 (2014) - [j69]Alex Seret, Thomas Verbraken, Bart Baesens:
A new knowledge-based constrained clustering approach: Theory and application in direct marketing. Appl. Soft Comput. 24: 316-327 (2014) - [j68]Filip Caron, Jan Vanthienen, Kris Vanhaecht, Erik van Limbergen, Jochen De Weerdt, Bart Baesens:
Monitoring care processes in the gynecologic oncology department. Comput. Biol. Medicine 44: 88-96 (2014) - [j67]Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens:
Predicting online channel acceptance with social network data. Decis. Support Syst. 63: 104-114 (2014) - [j66]Thomas Verbraken, Cristián Bravo, Richard Weber, Bart Baesens:
Development and application of consumer credit scoring models using profit-based classification measures. Eur. J. Oper. Res. 238(2): 505-513 (2014) - [j65]Alex Seret, Seppe K. L. M. vanden Broucke, Bart Baesens, Jan Vanthienen:
A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Syst. Appl. 41(10): 4648-4657 (2014) - [j64]Thomas Verbraken, Wouter Verbeke, Bart Baesens:
Profit optimizing customer churn prediction with Bayesian network classifiers. Intell. Data Anal. 18(1): 3-24 (2014) - [j63]Baojun Ma, Huaping Zhang, Guoqing Chen, Yanping Zhao, Bart Baesens:
Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective. Int. J. Softw. Eng. Knowl. Eng. 24(1): 61-90 (2014) - [j62]Filip Caron, Jan Vanthienen, Bart Baesens:
Clinical Pathway Analytics. J. Inf. Technol. Res. 7(1): 12-26 (2014) - [j61]Ellen Tobback, David Martens, Tony Van Gestel, Bart Baesens:
Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state. J. Oper. Res. Soc. 65(3): 376-392 (2014) - [j60]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE Trans. Knowl. Data Eng. 26(8): 1877-1889 (2014) - [c47]Xinwei Zhu, Guobin Zhu, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Towards Location-Aware Process Modeling and Execution. Business Process Management Workshops 2014: 186-197 - [c46]Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Declarative process discovery with evolutionary computing. IEEE Congress on Evolutionary Computation 2014: 2412-2419 - [c45]Aimée Backiel, Bart Baesens, Gerda Claeskens:
Mining Telecommunication Networks to Enhance Customer Lifetime Predictions. ICAISC (2) 2014: 15-26 - [c44]Seppe K. L. M. vanden Broucke, Jorge Munoz-Gama, Josep Carmona, Bart Baesens, Jan Vanthienen:
Event-Based Real-Time Decomposed Conformance Analysis. OTM Conferences 2014: 345-363 - 2013
- [j59]Jochen De Weerdt, Annelies Schupp, An Vanderloock, Bart Baesens:
Process Mining for the multi-faceted analysis of business processes - A case study in a financial services organization. Comput. Ind. 64(1): 57-67 (2013) - [j58]Filip Caron, Jan Vanthienen, Bart Baesens:
A comprehensive investigation of the applicability of process mining techniques for enterprise risk management. Comput. Ind. 64(4): 464-475 (2013) - [j57]Filip Caron, Jan Vanthienen, Bart Baesens:
Comprehensive rule-based compliance checking and risk management with process mining. Decis. Support Syst. 54(3): 1357-1369 (2013) - [j56]Helen-Tadesse Moges, Karel Dejaeger, Wilfried Lemahieu, Bart Baesens:
A multidimensional analysis of data quality for credit risk management: New insights and challenges. Inf. Manag. 50(1): 43-58 (2013) - [j55]Thomas Verbraken, Wouter Verbeke, Bart Baesens:
A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models. IEEE Trans. Knowl. Data Eng. 25(5): 961-973 (2013) - [j54]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Active Trace Clustering for Improved Process Discovery. IEEE Trans. Knowl. Data Eng. 25(12): 2708-2720 (2013) - [j53]Karel Dejaeger, Thomas Verbraken, Bart Baesens:
Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers. IEEE Trans. Software Eng. 39(2): 237-257 (2013) - [c43]Véronique Van Vlasselaer, Jan Meskens, Dries Van Dromme, Bart Baesens:
Using social network knowledge for detecting spider constructions in social security fraud. ASONAM 2013: 813-820 - [c42]Seppe K. L. M. vanden Broucke, Cédric Delvaux, João Freitas, Taisiia Rogova, Jan Vanthienen, Bart Baesens:
Uncovering the Relationship Between Event Log Characteristics and Process Discovery Techniques. Business Process Management Workshops 2013: 41-53 - [c41]Seppe K. L. M. vanden Broucke, Filip Caron, Jan Vanthienen, Bart Baesens:
Validating and Enhancing Declarative Business Process Models Based on Allowed and Non-occurring Past Behavior. Business Process Management Workshops 2013: 212-223 - [c40]Seppe vanden Broucke, Jan Vanthienen, Bart Baesens:
Volvo IT Belgium VINST. BPIC@BPM 2013 - [c39]Alex Seret, Seppe K. L. M. vanden Broucke, Bart Baesens, Jan Vanthienen:
An Exploratory Approach for Understanding Customer Behavior Processes Based on Clustering and Sequence Mining. Business Process Management Workshops 2013: 237-248 - [c38]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. CIDM 2013: 254-261 - [c37]Filip Caron, Jan Vanthienen, Bart Baesens:
Business Rule Patterns and Their Application to Process Analytics. EDOC Workshops 2013: 13-20 - [c36]Libo Li, Frank Goethals, Antonio Giangreco, Bart Baesens:
Using social network data to predict technology acceptance. ICIS 2013 - 2012
- [j52]Wouter Verbeke, Karel Dejaeger, David Martens, Joon Hur, Bart Baesens:
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. Eur. J. Oper. Res. 218(1): 211-229 (2012) - [j51]Karel Dejaeger, Frank Goethals, Antonio Giangreco, Lapo Mola, Bart Baesens:
Gaining insight into student satisfaction using comprehensible data mining techniques. Eur. J. Oper. Res. 218(2): 548-562 (2012) - [j50]Alex Seret, Thomas Verbraken, Sébastien Versailles, Bart Baesens:
A new SOM-based method for profile generation: Theory and an application in direct marketing. Eur. J. Oper. Res. 220(1): 199-209 (2012) - [j49]Helen-Tadesse Moges, Karel Dejaeger, Wilfried Lemahieu, Bart Baesens:
A total data quality management for credit risk: new insights and challenges. Int. J. Inf. Qual. 3(1): 1-27 (2012) - [j48]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7): 654-676 (2012) - [j47]Bart Baesens, Pantelis Bouboulis, Sergio Cruces, Carlotta Domeniconi, Shiro Ikeda, Xuelong Li, Patricia Melin, Vadrevu Sree Hari Rao, Björn W. Schuller, Yi Shen, Huajin Tang, Cong Wang, Jian Yang, Derong Zhao, Derong Liu:
Neural Networks and Learning Systems Come Together. IEEE Trans. Neural Networks Learn. Syst. 23(1): 1-6 (2012) - [j46]Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens:
Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Trans. Software Eng. 38(2): 375-397 (2012) - [c35]Filip Caron, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
On the Distinction between Truthful, Invisible, False and Unobserved Events An Event Existence Classification Framework and the Impact on Business Process Analytics Related Research Areas. AMCIS 2012 - [c34]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Bart Baesens, Jan Vanthienen:
Improved Artificial Negative Event Generation to Enhance Process Event Logs. CAiSE 2012: 254-269 - [c33]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c32]Filip Caron, Jan Vanthienen, Bart Baesens:
Rule-Based Business Process Mining: Applications for Management. IS-MiS 2012: 273-282 - [c31]Jochen De Weerdt, Filip Caron, Jan Vanthienen, Bart Baesens:
Getting a Grasp on Clinical Pathway Data: An Approach Based on Process Mining. PAKDD Workshops 2012: 22-35 - 2011
- [j45]Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart Baesens:
Process discovery in event logs: An application in the telecom industry. Appl. Soft Comput. 11(2): 1697-1710 (2011) - [j44]Johan Huysmans, Karel Dejaeger, Christophe Mues, Jan Vanthienen, Bart Baesens:
An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decis. Support Syst. 51(1): 141-154 (2011) - [j43]David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens:
Performance of classification models from a user perspective. Decis. Support Syst. 51(4): 782-793 (2011) - [j42]Elen Lima, Christophe Mues, Bart Baesens:
Monitoring and backtesting churn models. Expert Syst. Appl. 38(1): 975-982 (2011) - [j41]Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens:
Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38(3): 2354-2364 (2011) - [j40]David Martens, Christine Vanhoutte, Sophie De Winne, Bart Baesens, Luc Sels, Christophe Mues:
Identifying financially successful start-up profiles with data mining. Expert Syst. Appl. 38(5): 5794-5800 (2011) - [j39]Rudy Setiono, Bart Baesens, Christophe Mues:
Rule Extraction from Minimal Neural Networks for Credit Card Screening. Int. J. Neural Syst. 21(4): 265-276 (2011) - [j38]David Martens, Bart Baesens, Tom Fawcett:
Editorial survey: swarm intelligence for data mining. Mach. Learn. 82(1): 1-42 (2011) - [j37]Bart Baesens, David Martens, Rudy Setiono, Jacek M. Zurada:
Guest Editorial White Box Nonlinear Prediction Models. IEEE Trans. Neural Networks 22(12): 2406-2408 (2011) - [c30]Filip Caron, Jan Vanthienen, Jochen De Weerdt, Bart Baesens:
Advanced Care-Flow Mining and Analysis. Business Process Management Workshops (1) 2011: 167-168 - [c29]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A robust F-measure for evaluating discovered process models. CIDM 2011: 148-155 - [c28]Helen-Tadesse Moges, Karel Dejaeger, Wilfried Lemahieu, Bart Baesens:
Data quality for credit risk management. ICIQ 2011 - [c27]Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens:
Using Social Network Classifiers for Predicting E-Commerce Adoption. WEB 2011: 9-21 - 2010
- [j36]Tony Van Gestel, Bart Baesens, David Martens:
From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing 73(16-18): 2955-2970 (2010) - [j35]G. Castermans, David Martens, Tony Van Gestel, Bart Hamers, Bart Baesens:
An overview and framework for PD backtesting and benchmarking. J. Oper. Res. Soc. 61(3): 359-373 (2010) - [j34]David Martens, Tony Van Gestel, Manu De Backer, Raf Haesen, Jan Vanthienen, Bart Baesens:
Credit rating prediction using Ant Colony Optimization. J. Oper. Res. Soc. 61(4): 561-573 (2010) - [c26]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A Critical Evaluation Study of Model-Log Metrics in Process Discovery. Business Process Management Workshops 2010: 158-169 - [c25]Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens:
Software Effort Prediction Using Regression Rule Extraction from Neural Networks. ICTAI (2) 2010: 45-52 - [c24]Karel Dejaeger, Bart Hamers, Jonas Poelmans, Bart Baesens:
A novel approach to the evaluation and improvement of data quality in the financial sector. ICIQ 2010 - [p4]David Martens, Bart Baesens:
Building Acceptable Classification Models. Data Mining 2010: 53-74
2000 – 2009
- 2009
- [j33]Rudy Setiono, Bart Baesens, Christophe Mues:
A note on knowledge discovery using neural networks and its application to credit card screening. Eur. J. Oper. Res. 192(1): 326-332 (2009) - [j32]Nicolas Glady, Bart Baesens, Christophe Croux:
Modeling churn using customer lifetime value. Eur. J. Oper. Res. 197(1): 402-411 (2009) - [j31]Nicolas Glady, Bart Baesens, Christophe Croux:
A modified Pareto/NBD approach for predicting customer lifetime value. Expert Syst. Appl. 36(2): 2062-2071 (2009) - [j30]Bjorn Cumps, David Martens, Manu De Backer, Raf Haesen, Stijn Viaene, Guido Dedene, Bart Baesens, Monique Snoeck:
Inferring comprehensible business/ICT alignment rules. Inf. Manag. 46(2): 116-124 (2009) - [j29]Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens:
Robust Process Discovery with Artificial Negative Events. J. Mach. Learn. Res. 10: 1305-1340 (2009) - [j28]Elen Lima, Christophe Mues, Bart Baesens:
Domain knowledge integration in data mining using decision tables: case studies in churn prediction. J. Oper. Res. Soc. 60(8): 1096-1106 (2009) - [j27]Bart Baesens, Christophe Mues, David Martens, Jan Vanthienen:
50 years of data mining and OR: upcoming trends and challenges. J. Oper. Res. Soc. 60(S1) (2009) - [j26]David Martens, Bart Baesens, Tony Van Gestel:
Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Trans. Knowl. Data Eng. 21(2): 178-191 (2009) - [c23]Patrick Wessa, Bart Baesens:
Fraud Detection in Statistics Education Based on the Compendium Platform and Reproducible Computing. CSIE (3) 2009: 50-54 - [c22]Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen:
Including Domain Knowledge in Customer Churn Prediction Using AntMiner+. DMM@ICDM 2009: 10-21 - 2008
- [j25]David Martens, Liesbeth Bruynseels, Bart Baesens, Marleen Willekens, Jan Vanthienen:
Predicting going concern opinion with data mining. Decis. Support Syst. 45(4): 765-777 (2008) - [j24]Olivier Vandecruys, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, Raf Haesen:
Mining software repositories for comprehensible software fault prediction models. J. Syst. Softw. 81(5): 823-839 (2008) - [j23]Rudy Setiono, Bart Baesens, Christophe Mues:
Recursive Neural Network Rule Extraction for Data With Mixed Attributes. IEEE Trans. Neural Networks 19(2): 299-307 (2008) - [j22]Stefan Lessmann, Bart Baesens, Christophe Mues, Swantje Pietsch:
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings. IEEE Trans. Software Eng. 34(4): 485-496 (2008) - [j21]Johan Huysmans, Rudy Setiono, Bart Baesens, Jan Vanthienen:
Minerva: Sequential Covering for Rule Extraction. IEEE Trans. Syst. Man Cybern. Part B 38(2): 299-309 (2008) - [p3]David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens:
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. Rule Extraction from Support Vector Machines 2008: 33-63 - [p2]Johan Huysmans, Bart Baesens, Jan Vanthienen:
A Data Miner's Approach to Country Corruption Analysis. Intelligence and Security Informatics 2008: 227-247 - 2007
- [j20]Johan Huysmans, Bart Baesens, Jan Vanthienen:
A new approach for measuring rule set consistency. Data Knowl. Eng. 63(1): 167-182 (2007) - [j19]Frank Hoffmann, Bart Baesens, Christophe Mues, Tony Van Gestel, Jan Vanthienen:
Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. Eur. J. Oper. Res. 177(1): 540-555 (2007) - [j18]David Martens, Bart Baesens, Tony Van Gestel, Jan Vanthienen:
Comprehensible credit scoring models using rule extraction from support vector machines. Eur. J. Oper. Res. 183(3): 1466-1476 (2007) - [j17]David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens:
Classification With Ant Colony Optimization. IEEE Trans. Evol. Comput. 11(5): 651-665 (2007) - [c21]Stijn Goedertier, David Martens, Bart Baesens, Raf Haesen, Jan Vanthienen:
Process Mining as First-Order Classification Learning on Logs with Negative Events. Business Process Management Workshops 2007: 42-53 - 2006
- [j16]Tony Van Gestel, Bart Baesens, Peter Van Dijcke, Joao Garcia, Johan A. K. Suykens, Jan Vanthienen:
A process model to develop an internal rating system: Sovereign credit ratings. Decis. Support Syst. 42(2): 1131-1151 (2006) - [j15]Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Dirk Van den Poel, Dirk-Emma Baestaens, Marleen Willekens:
Bayesian kernel based classification for financial distress detection. Eur. J. Oper. Res. 172(3): 979-1003 (2006) - [j14]Bart Baesens, Christophe Mues, Tony Van Gestel, Jan Vanthienen:
Special issue on intelligent information systems for financial engineering. Expert Syst. Appl. 30(3): 413-414 (2006) - [j13]Johan Huysmans, Bart Baesens, Jan Vanthienen, Tony Van Gestel:
Failure prediction with self organizing maps. Expert Syst. Appl. 30(3): 479-487 (2006) - [c20]David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Christophe Mues, Jan Vanthienen:
Ant-Based Approach to the Knowledge Fusion Problem. ANTS Workshop 2006: 84-95 - [c19]Johan Huysmans, Bart Baesens, Jan Vanthienen:
ITER: An Algorithm for Predictive Regression Rule Extraction. DaWaK 2006: 270-279 - [c18]Rudy Setiono, Christophe Mues, Bart Baesens:
Risk Management and Regulatory Compliance: A Data Mining Framework Based on Neural Network Rule Extraction. ICIS 2006: 7 - [c17]Johan Huysmans, David Martens, Bart Baesens, Jan Vanthienen, Tony Van Gestel:
Country Corruption Analysis with Self Organizing Maps and Support Vector Machines. WISI 2006: 103-114 - [p1]David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Tom Holvoet:
Ants Constructing Rule-Based Classifiers. Swarm Intelligence in Data Mining 2006: 21-43 - 2005
- [j12]Michael Egmont-Petersen, A. J. Feelders, Bart Baesens:
Confidence intervals for probabilistic network classifiers. Comput. Stat. Data Anal. 49(4): 998-1019 (2005) - [j11]Petr Somol, Bart Baesens, Pavel Pudil, Jan Vanthienen:
Filter- versus wrapper-based feature selection for credit scoring. Int. J. Intell. Syst. 20(10): 985-999 (2005) - [j10]Bart Baesens, Tony Van Gestel, M. Stepanova, Dirk Van den Poel, Jan Vanthienen:
Neural network survival analysis for personal loan data. J. Oper. Res. Soc. 56(9): 1089-1098 (2005) - [c16]Manu De Backer, Raf Haesen, David Martens, Bart Baesens:
A Stigmergy Based Approach to Data Mining. Australian Conference on Artificial Intelligence 2005: 975-978 - [c15]Johan Huysmans, Bart Baesens, Jan Vanthienen:
A Comprehensible SOM-Based Scoring System. MLDM 2005: 80-89 - [c14]Christophe Mues, Bart Baesens, Jan Vanthienen:
From Knowledge Discovery to Implementation: Developing Business Intelligence Systems using Decision Tables. Wissensmanagement 2005: 439-443 - [c13]Christophe Mues, Bart Baesens, Rudy Setiono, Jan Vanthienen:
From Knowledge Discovery to Implementation: A Business Intelligence Approach Using Neural Network Rule Extraction and Decision Tables. Wissensmanagement (LNCS Volume) 2005: 483-495 - 2004
- [j9]Bart Baesens, Geert Verstraeten, Dirk Van den Poel, Michael Egmont-Petersen, Patrick Van Kenhove, Jan Vanthienen:
Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. Eur. J. Oper. Res. 156(2): 508-523 (2004) - [j8]Christophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen:
Decision diagrams in machine learning: an empirical study on real-life credit-risk data. Expert Syst. Appl. 27(2): 257-264 (2004) - [j7]Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle:
Benchmarking Least Squares Support Vector Machine Classifiers. Mach. Learn. 54(1): 5-32 (2004) - [c12]Christophe Mues, Bart Baesens, Craig M. Files, Jan Vanthienen:
Decision Diagrams in Machine Learning: An Empirical Study on Real-Life Credit-Risk Data. Diagrams 2004: 395-397 - [c11]Christophe Mues, Johan Huysmans, Jan Vanthienen, Bart Baesens:
Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables and Diagrams. ICEIS (Selected Papers) 2004: 109-115 - [c10]Christophe Mues, Johan Huysmans, Jan Vanthienen, Bart Baesens:
Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables and Diagrams. ICEIS (2) 2004: 226-232 - [c9]Johan Huysmans, Christophe Mues, Jan Vanthienen, Bart Baesens:
Web Usage Mining with Time Constrained Association Rules. ICEIS (2) 2004: 343-348 - 2003
- [j6]Bart Baesens, Tony Van Gestel, Stijn Viaene, M. Stepanova, Johan A. K. Suykens, Jan Vanthienen:
Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6): 627-635 (2003) - [j5]Bart Baesens, Rudy Setiono, Christophe Mues, Jan Vanthienen:
Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation. Manag. Sci. 49(3): 312-329 (2003) - [c8]Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Marcelo Espinoza, Dirk-Emma Baestaens, Jan Vanthienen, Bart De Moor:
Bankruptcy prediction with least squares support vector machine classifiers. CIFEr 2003: 1-8 - [c7]Bart Baesens, Christophe Mues, Manu De Backer, Jan Vanthienen, Rudy Setiono:
Building Intelligent Credit Scoring Systems Using Decision Tables. ICEIS (2) 2003: 19-25 - 2002
- [j4]Bart Baesens, Stijn Viaene, Dirk Van den Poel, Jan Vanthienen, Guido Dedene:
Bayesian neural network learning for repeat purchase modelling in direct marketing. Eur. J. Oper. Res. 138(1): 191-211 (2002) - [j3]Frank Hoffmann, Bart Baesens, Jurgen Martens, Ferdi Put, Jan Vanthienen:
Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring. Int. J. Intell. Syst. 17(11): 1067-1083 (2002) - [c6]Stijn Viaene, Bart Baesens, Guido Dedene, Jan Vanthienen, Dirk Van den Poel:
Proof Running Two State-Of-The-Art Pattern Recognition Techniques in the Field of Direct Marketing. ICEIS 2002: 446-454 - [c5]Bart Baesens, Michael Egmont-Petersen, Robert Castelo, Jan Vanthienen:
Learning Bayesian Network Classifiers for Credit Scoring Using Markov Chain Monte Carlo Search. ICPR (3) 2002: 49-52 - 2001
- [j2]Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene:
Knowledge discovery in a direct marketing case using least squares support vector machines. Int. J. Intell. Syst. 16(9): 1023-1036 (2001) - [j1]Stijn Viaene, Bart Baesens, Dirk Van den Poel, Guido Dedene, Jan Vanthienen:
Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing. Intell. Syst. Account. Finance Manag. 10(2): 115-126 (2001) - [c4]Bart Baesens, Rudy Setiono, Christophe Mues, Stijn Viaene, Jan Vanthienen:
Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables. ICIS 2001: 159-168 - 2000
- [c3]Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene:
Wrapped Feature Selection by Means of Guided Neural Network Optimization. ICPR 2000: 2113-2116 - [c2]Bart Baesens, Stijn Viaene, Tony Van Gestel, Johan A. K. Suykens, Guido Dedene, Bart De Moor, Jan Vanthienen:
An empirical assessment of kernel type performance for least squares support vector machine classifiers. KES 2000: 313-316 - [c1]Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene:
Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. PKDD 2000: 657-664
Coauthor Index
Seppe K. L. M. vanden Broucke
aka: Seppe vanden Broucke
aka: Seppe vanden Broucke
María Óskarsdóttir
aka: Maria Óskarsdóttir
aka: Maria Óskarsdóttir
[i10] [i9] [i8] [i6] [j110] [j108] [j105] [j103] [j100] [j99] [c60] [j89] [j88] [j83] [j81] [j79] [c53] [j68] [j65] [j62] [j60] [c47] [c46] [c44] [j58] [j57] [j54] [c42] [c41] [c40] [c39] [c38] [c37] [j48] [c35] [c34] [c33] [c32] [c31] [j45] [j44] [j43] [c30] [c29] [j34] [c26] [j29] [j27] [j25] [j21] [p3] [p2] [j20] [j19] [j18] [j17] [c21] [j16] [j14] [j13] [c20] [c19] [c17] [j11] [j10] [c15] [c14] [c13] [j9] [j8] [j7] [c12] [c11] [c10] [c9] [j6] [j5] [c8] [c7] [j4] [j3] [c6] [c5] [j2] [j1] [c4] [c3] [c2] [c1]
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