World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
41
Citations
7838
World Ranking
6887
National Ranking
88

David Martens publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where David Martens sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 150 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

David Martens D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where David Martens sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 41 D-Index — 31st percentile

31% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

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

  • 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

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

    Wouter Verbeke;David Martens;Christophe Mues;Bart Baesens

  • Explaining data-driven document classifications

    David Martens;Foster Provost

  • Editorial survey: swarm intelligence for data mining

    David Martens;Bart Baesens;Tom Fawcett

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

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

  • Predictive Modeling With Big Data: Is Bigger Really Better?

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

  • Benchmarking regression algorithms for loss given default modeling

    Gert Loterman;Iain Brown;David Martens;Christophe Mues

  • Decompositional Rule Extraction from Support Vector Machines by Active Learning

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

  • Robust Process Discovery with Artificial Negative Events

    Stijn Goedertier;David Martens;Jan Vanthienen;Bart Baesens

  • Social network analysis for customer churn prediction

    Wouter Verbeke;David Martens;Bart Baesens;Bart Baesens

  • Mining massive fine-grained behavior data to improve predictive analytics

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

  • Predicting going concern opinion with data mining

    David Martens;Liesbeth Bruynseels;Bart Baesens;Marleen Willekens

  • Mining software repositories for comprehensible software fault prediction models

    Olivier Vandecruys;David Martens;Bart Baesens;Christophe Mues

  • Performance of classification models from a user perspective

    David Martens;Jan Vanthienen;Wouter Verbeke;Bart Baesens

  • Evaluating and understanding text-based stock price prediction models

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

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

    David Martens;Johan Huysmans;Rudy Setiono;Jan Vanthienen

  • Bankruptcy prediction for SMEs using relational data

    Ellen Tobback;Tony Bellotti;Julie Moeyersoms;Marija Stankova

  • Process discovery in event logs: An application in the telecom industry

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

Frequent Co-Authors

Bart Baesens
Bart Baesens KU Leuven
Foster Provost
Foster Provost New York University
Walter Daelemans
Walter Daelemans University of Antwerp
Rudy Setiono
Rudy Setiono National University of Singapore
Peter Van Aelst
Peter Van Aelst University of Antwerp
Kenneth Sörensen
Kenneth Sörensen University of Antwerp
Luc Sels
Luc Sels KU Leuven
Stefaan Walgrave
Stefaan Walgrave University of Antwerp

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