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Bart Baesens

Bart Baesens

D-Index & Metrics

Computer Science

D-Index
76
Citations
22762
World Ranking
1345
National Ranking
18

Overview

Bart Baesens is affiliated with KU Leuven in Belgium and has contributed extensively to the field of Computer Science. Their research primarily focuses on Artificial Intelligence, Information Systems, Management Science and Operations Research, Management Information Systems, and Sociology and Political Science.

The primary areas of study covered in their work include:

  • Imbalanced Data Classification Techniques
  • Anomaly Detection Techniques and Applications
  • Financial Distress and Bankruptcy Prediction
  • Sentiment Analysis and Opinion Mining
  • Topic Modeling
  • Explainable Artificial Intelligence (XAI)
  • Forecasting Techniques and Applications

Bart Baesens has authored multiple papers published in various academic journals and conferences. Some of their recent papers are:

  • Data engineering for fraud detection, 2021, Decision Support Systems
  • Deep learning for credit scoring: Do or don't?, 2021, European Journal of Operational Research
  • Special issue on feature engineering editorial, 2021, Machine Learning
  • Profit Driven Decision Trees for Churn Prediction, 2020, Lirias (KU Leuven)
  • Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda, 2023, European Journal of Operational Research

The venues where Bart Baesens frequently publishes include:

  • arXiv (Cornell University)
  • Decision Support Systems
  • European Journal of Operational Research
  • Proceedings of the Annual Hawaii International Conference on System Sciences
  • Expert Systems with Applications

Collaborations have also been a part of their academic activity. Frequent co-authors include:

  • Tim Verdonck
  • Wouter Verbeke
  • María Óskarsdóttir
  • Manon Reusens
  • Monique Snoeck

Bart Baesens has not been documented as receiving any awards. Their body of work emphasizes application-driven research within operational research and machine learning fields, focusing on the challenges and methods related to data engineering, credit scoring, fraud detection, and explainability in artificial intelligence models.

Best Publications

  • Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

    S. Lessmann;B. Baesens;C. Mues;S. Pietsch

  • Benchmarking state-of-the-art classification algorithms for credit scoring

    B Baesens;T Van Gestel;S Viaene;M Stepanova

  • Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

    Stefan Lessmann;Bart Baesens;Bart Baesens;Hsin-Vonn Seow;Lyn C. Thomas

  • Benchmarking Least Squares Support Vector Machine Classifiers

    Tony Van Gestel;Johan A. K. Suykens;Bart Baesens;Stijn Viaene

  • Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

    Bart Baesens;Rudy Setiono;Christophe Mues;Jan Vanthienen

  • Comprehensible credit scoring models using rule extraction from support vector machines

    David Martens;Bart Baesens;Bart Baesens;Tony Van Gestel;Jan Vanthienen

  • New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

    Wouter Verbeke;Karel Dejaeger;David Martens;Joon Hur

  • Classification With Ant Colony Optimization

    D. Martens;M. De Backer;R. Haesen;J. Vanthienen

  • An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

    Johan Huysmans;Karel Dejaeger;Christophe Mues;Jan Vanthienen

  • Building comprehensible customer churn prediction models with advanced rule induction techniques

    Wouter Verbeke;David Martens;Christophe Mues;Bart Baesens

  • APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions

    Véronique Van Vlasselaer;Cristián Bravo;Olivier Caelen;Tina Eliassi-Rad

  • Transformational issues of big data and analytics in networked business

    Bart Baesens;Ravi Bapna;James R. Marsden;Jan Vanthienen

  • Analytics In A Big Data World: The Essential Guide To Data Science And Its Applications

    Bart Baesens

  • A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection

    Stijn Viaene;Richard A. Derrig;Bart Baesens;Guido Dedene

  • Editorial survey: swarm intelligence for data mining

    David Martens;Bart Baesens;Tom Fawcett

  • Modeling churn using customer lifetime value

    Nicolas Glady;Bart Baesens;Bart Baesens;Christophe Croux

  • Data Mining Techniques for Software Effort Estimation: A Comparative Study

    K. Dejaeger;W. Verbeke;D. Martens;B. Baesens

  • Bayesian neural network learning for repeat purchase modelling in direct marketing

    Bart Baesens;Stijn Viaene;Dirk Van den Poel;Jan Vanthienen

  • A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

    Jochen De Weerdt;Manu De Backer;Jan Vanthienen;Bart Baesens

  • Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers

    K. Dejaeger;T. Verbraken;B. Baesens

Frequent Co-Authors

David Martens
David Martens University of Antwerp
Rudy Setiono
Rudy Setiono National University of Singapore
Luc Sels
Luc Sels KU Leuven
Dirk Van den Poel
Dirk Van den Poel Ghent University
Stefan Lessmann
Stefan Lessmann Humboldt-Universität zu Berlin
Tina Eliassi-Rad
Tina Eliassi-Rad Northeastern University
Leman Akoglu
Leman Akoglu Carnegie Mellon University
Jacek M. Zurada
Jacek M. Zurada University of Louisville

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