2026 David Martens: Engineering and Technology Researcher – H-Index, Publications & Awards

D-Index & Metrics

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Engineering and Technology	41	6884	6645	88	88	150	7838

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Engineering and Technology

6884

6645

150

7838

Engineering and Technology

D-Index

Citations

7838

World Ranking

6884

National Ranking

Overview

David Martens is affiliated with the University of Antwerp in Belgium. Their research primarily falls within the field of Computer Science, with a strong focus on Artificial Intelligence, as well as Information Systems and Management. They also contribute to the areas of Communication, Safety Research, and Management Science and Operations Research.

The scientist's work centers on several key topics, including:

Explainable Artificial Intelligence (XAI)
Adversarial Robustness in Machine Learning
Machine Learning and Data Classification
Scientific Computing and Data Management
Social Media and Politics
Ethics and Social Impacts of AI
Big Data and Business Intelligence

Recent publications by David Martens include:

"Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda" (2023), published in European Journal of Operational Research
"Explainable image classification with evidence counterfactual" (2022), published in Pattern Analysis and Applications
"NICE: an algorithm for nearest instance counterfactual explanations" (2023), published in Data Mining and Knowledge Discovery
"Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records" (2021), published in Information
"A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data" (2021), published in Applied Sciences

David Martens frequently publishes in several academic venues, including:

arXiv (Cornell University)
SSRN Electronic Journal
European Journal of Operational Research
Online Social Networks and Media
Machine Learning

The scientist often collaborates with coauthors such as Sofie Goethals, Tom Vermeire, Dieter Brughmans, Stiene Praet, and Raphael Oliveira, reflecting a network of recurring academic partnerships.

Best Publications

Comprehensible credit scoring models using rule extraction from support vector machines

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

575
Citations
New insights into churn prediction in the telecommunication sector: a profit driven data mining approach

Wouter Verbeke;Karel Dejaeger;David Martens;Joon Hur

573
Citations
Classification With Ant Colony Optimization

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

570
Citations
Building comprehensible customer churn prediction models with advanced rule induction techniques

Wouter Verbeke;David Martens;Christophe Mues;Bart Baesens

464
Citations
Explaining data-driven document classifications

David Martens;Foster Provost

350
Citations
Editorial survey: swarm intelligence for data mining

David Martens;Bart Baesens;Tom Fawcett

293
Citations
Data Mining Techniques for Software Effort Estimation: A Comparative Study

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

289
Citations
Predictive Modeling With Big Data: Is Bigger Really Better?

Enric Junqué de Fortuny;David Martens;Foster J. Provost

252
Citations
Benchmarking regression algorithms for loss given default modeling

Gert Loterman;Iain Brown;David Martens;Christophe Mues

240
Citations
Decompositional Rule Extraction from Support Vector Machines by Active Learning

D. Martens;B.B. Baesens;T. Van Gestel

218
Citations
Robust Process Discovery with Artificial Negative Events

Stijn Goedertier;David Martens;Jan Vanthienen;Bart Baesens

209
Citations
Social network analysis for customer churn prediction

Wouter Verbeke;David Martens;Bart Baesens;Bart Baesens

200
Citations
Mining massive fine-grained behavior data to improve predictive analytics

David Martens;Foster Provost;Jessica Clark;Enric Junqué de Fortuny

182
Citations
Predicting going concern opinion with data mining

David Martens;Liesbeth Bruynseels;Bart Baesens;Marleen Willekens

179
Citations
Mining software repositories for comprehensible software fault prediction models

Olivier Vandecruys;David Martens;Bart Baesens;Christophe Mues

166
Citations
Performance of classification models from a user perspective

David Martens;Jan Vanthienen;Wouter Verbeke;Bart Baesens

149
Citations
Evaluating and understanding text-based stock price prediction models

Enric Junqué De Fortuny;Tom De Smedt;David Martens;Walter Daelemans

145
Citations
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring

David Martens;Johan Huysmans;Rudy Setiono;Jan Vanthienen

140
Citations
Bankruptcy prediction for SMEs using relational data

Ellen Tobback;Tony Bellotti;Julie Moeyersoms;Marija Stankova

134
Citations
Process discovery in event logs: An application in the telecom industry

Stijn Goedertier;Jochen De Weerdt;David Martens;Jan Vanthienen

102
Citations