David Martens

4.3k total citations
76 papers, 2.8k citations indexed

About

David Martens is a scholar working on Artificial Intelligence, Information Systems and Marketing. According to data from OpenAlex, David Martens has authored 76 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 17 papers in Information Systems and 12 papers in Marketing. Recurrent topics in David Martens's work include Imbalanced Data Classification Techniques (18 papers), Explainable Artificial Intelligence (XAI) (15 papers) and Machine Learning and Data Classification (10 papers). David Martens is often cited by papers focused on Imbalanced Data Classification Techniques (18 papers), Explainable Artificial Intelligence (XAI) (15 papers) and Machine Learning and Data Classification (10 papers). David Martens collaborates with scholars based in Belgium, United States and United Kingdom. David Martens's co-authors include Bart Baesens, Wouter Verbeke, Tony Van Gestel, Foster Provost, Jan Vanthienen, Karel Dejaeger, Enric Junqué de Fortuny, Joon Hur, Christophe Mues and Walter Daelemans and has published in prestigious journals such as European Journal of Operational Research, MIS Quarterly and Expert Systems with Applications.

In The Last Decade

David Martens

74 papers receiving 2.6k citations

Peers

David Martens
Stefan Lessmann Germany
Christophe Mues United Kingdom
Wouter Verbeke Belgium
Stijn Viaene Belgium
Parag C. Pendharkar United States
Guido Dedene Belgium
Qing Cao United States
Vasant Dhar United States
Atish P. Sinha United States
Prabuddha De United States
Stefan Lessmann Germany
David Martens
Citations per year, relative to David Martens David Martens (= 1×) peers Stefan Lessmann

Countries citing papers authored by David Martens

Since Specialization
Citations

This map shows the geographic impact of David Martens's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Martens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Martens more than expected).

Fields of papers citing papers by David Martens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Martens. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Martens. The network helps show where David Martens may publish in the future.

Co-authorship network of co-authors of David Martens

This figure shows the co-authorship network connecting the top 25 collaborators of David Martens. A scholar is included among the top collaborators of David Martens based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Martens. David Martens is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Goethals, Sofie, et al.. (2025). Monetization could corrupt algorithmic explanations. AI & Society. 40(8). 6291–6308.
2.
Martens, David, et al.. (2024). Disagreement amongst counterfactual explanations: how transparency can be misleading. Top. 32(3). 429–462. 6 indexed citations
3.
Bock, Koen W. De, Kristof Coussement, Arno De Caigny, et al.. (2023). Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. European Journal of Operational Research. 317(2). 249–272. 61 indexed citations
4.
Goethals, Sofie, Kenneth Sörensen, & David Martens. (2023). The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks. ACM Transactions on Intelligent Systems and Technology. 14(5). 1–24. 9 indexed citations
5.
Goethals, Sofie, David Martens, & Toon Calders. (2023). PreCoF: counterfactual explanations for fairness. Machine Learning. 113(5). 3111–3142. 7 indexed citations
6.
Martens, David, et al.. (2023). NICE: an algorithm for nearest instance counterfactual explanations. Data Mining and Knowledge Discovery. 38(5). 2665–2703. 17 indexed citations
7.
Goethals, Sofie, David Martens, & Theodoros Evgeniou. (2022). The non-linear nature of the cost of comprehensibility. Journal Of Big Data. 9(1). 7 indexed citations
8.
Aelst, Peter Van, et al.. (2021). Comparing automated content analysis methods to distinguish issue communication by political parties on Twitter. VUBIR (Vrije Universiteit Brussel). 3(2). 1–27. 2 indexed citations
9.
10.
Martens, David, et al.. (2020). Node classification over bipartite graphs through projection. Machine Learning. 110(1). 37–87. 5 indexed citations
11.
Martens, David, et al.. (2019). Deep Learning on Big, Sparse, Behavioral Data. Big Data. 7(4). 286–307. 6 indexed citations
12.
Verbeke, Wouter, David Martens, & Bart Baesens. (2017). RULEM: rule learning with monotonicity constraints for ordinal classification. Applied Soft Computing. 1 indexed citations
13.
Martens, David, et al.. (2016). Datamining voor Fraudedetectie. 7(2). 167. 1 indexed citations
14.
D'Alessandro, B, et al.. (2016). Explaining Classification Models Built on High-Dimensional Sparse Data. arXiv (Cornell University). 36–40. 1 indexed citations
15.
Martens, David & Foster Provost. (2013). Explaining Data-Driven Document Classifications. Journal of the Association for Information Systems. 8 indexed citations
16.
Martens, David & Foster Provost. (2011). Explaining Documents' Classifications. SSRN Electronic Journal. 2 indexed citations
17.
Martens, David & Foster Provost. (2011). Pseudo-social network targeting from consumer transaction data. The Faculty Digital Archive (New York University). 18 indexed citations
18.
Brown, Iain, et al.. (2009). Benchmarking state-of-the-art regression algorithms for loss given default modelling. ePrints Soton (University of Southampton). 1 indexed citations
19.
Goedertier, Stijn, David Martens, Jan Vanthienen, & Bart Baesens. (2009). Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research. 10(44). 1305–1340. 82 indexed citations
20.
Huysmans, Johan, et al.. (2005). New Trends in Data Mining. Lirias (KU Leuven). 697–711. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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