David Martens

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 50

Top Topics

Big Five Personality

Behavioral Data

Monotonicity Constraints

Top Venues

Decis. Support Syst.

Appl. Soft Comput.

Publications

Sofie Goethals, David Martens, Toon Calders
PreCoF: counterfactual explanations for fairness. Mach. Learn. 113 (5) (2024)
Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet
Can metafeatures help improve explanations of prediction models when using behavioral and textual data? Mach. Learn. 113 (7) (2024)
Raphael Mazzine Barbosa de Oliveira, Kenneth Sörensen, David Martens
A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data. Eur. J. Oper. Res. 317 (2) (2024)
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) (2024)
James Hinns, David Martens
Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables. CoRR (2024)
Sofie Goethals, Toon Calders, David Martens
Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics. CoRR (2024)
Travis Greene, Sofie Goethals, David Martens, Galit Shmueli
Monetizing Explainable AI: A Double-edged Sword. CoRR (2023)
Sofie Goethals, David Martens, Theodoros Evgeniou
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. CoRR (2023)
Raphael Mazzine Barbosa de Oliveira, Sofie Goethals, Dieter Brughmans, David Martens
Unveiling the Potential of Counterfactuals Explanations in Employability. CoRR (2023)
Bjorge Meulemeester, David Martens
How sustainable is "common" data science in terms of power consumption? Sustain. Comput. Informatics Syst. 38 (2023)
Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens
The Impact of Cloaking Digital Footprints on User Privacy and Personalization. CoRR (2023)
Sofie Goethals, Kenneth Sörensen, David Martens
The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks. ACM Trans. Intell. Syst. Technol. 14 (5) (2023)
Sofie Goethals, David Martens, Toon Calders
Explainability Methods to Detect and Measure Discrimination in Machine Learning Models. EWAF (2023)
David Martens, Camille Dams, James Hinns, Mark Vergouwen
Tell Me a Story! Narrative-Driven XAI with Large Language Models. CoRR (2023)
Dieter Brughmans, Lissa Melis, David Martens
Disagreement amongst counterfactual explanations: How transparency can be deceptive. CoRR (2023)
Stiene Praet, David Martens, Peter Van Aelst
Erratum to. Online Soc. Networks Media (2023)
Bjorge Meulemeester, Raphael Mazzine Barbosa de Oliveira, David Martens
Calculating and Visualizing Counterfactual Feature Importance Values. CoRR (2023)
Tom Vermeire, Dieter Brughmans, Sofie Goethals, Raphael Mazzine Barbossa de Oliveira, David Martens
Explainable image classification with evidence counterfactual. Pattern Anal. Appl. 25 (2) (2022)
Sofie Goethals, David Martens, Theodoros Evgeniou
The non-linear nature of the cost of comprehensibility. J. Big Data 9 (1) (2022)
Travis Greene, David Martens, Galit Shmueli
Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms. Nat. Mach. Intell. 4 (4) (2022)
Sofie Goethals, Kenneth Sörensen, David Martens
The privacy issue of counterfactual explanations: explanation linkage attacks. CoRR (2022)
Bjorge Meulemeester, David Martens
How sustainable is "common" data science in terms of power consumption? CoRR (2022)
Stiene Praet, Peter Van Aelst, Patrick van Erkel, Stephan Van der Veeken, David Martens
Predictive modeling to study lifestyle politics with Facebook likes. EPJ Data Sci. 10 (1) (2021)
Yanou Ramon, Tom Vermeire, Olivier Toubia, David Martens, Theodoros Evgeniou
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms. CoRR (2021)
Yanou Ramon, R. A. Farrokhnia, Sandra C. Matz, David Martens
Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records. Inf. 12 (12) (2021)
Marija Stankova, Stiene Praet, David Martens, Foster J. Provost
Node classification over bipartite graphs through projection. Mach. Learn. 110 (1) (2021)
Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki
How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. CoRR (2021)
Raphael Mazzine, David Martens
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data. CoRR (2021)
Stiene Praet, David Martens, Peter Van Aelst
Patterns of democracy? Social network analysis of parliamentary Twitter networks in 12 countries. Online Soc. Networks Media 24 (2021)
Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki
How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. PKDD/ECML Workshops (1) (2021)
Yanou Ramon, Sandra C. Matz, R. A. Farrokhnia, David Martens
Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data. CoRR (2021)
Dieter Brughmans, David Martens
NICE: An Algorithm for Nearest Instance Counterfactual Explanations. CoRR (2021)
Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet
Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data. CoRR (2020)
Sofie De Cnudde, David Martens, Theodoros Evgeniou, Foster J. Provost
A benchmarking study of classification techniques for behavioral data. Int. J. Data Sci. Anal. 9 (2) (2020)
Stiene Praet, David Martens
Efficient Parcel Delivery by Predicting Customers' Locations. Decis. Sci. 51 (5) (2020)
Jellis Vanhoeyveld, David Martens, Bruno Peeters
Customs fraud detection. Pattern Anal. Appl. 23 (3) (2020)
Jellis Vanhoeyveld, David Martens, Bruno Peeters
Value-added tax fraud detection with scalable anomaly detection techniques. Appl. Soft Comput. 86 (2020)
Tom Vermeire, David Martens
Explainable Image Classification with Evidence Counterfactual. CoRR (2020)
Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C. Adv. Data Anal. Classif. 14 (4) (2020)
Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost
Deep Learning on Big, Sparse, Behavioral Data. Big Data 7 (4) (2019)
Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
Counterfactual Explanation Algorithms for Behavioral and Textual Data. CoRR (2019)
Michael Klum, Fabian Leib, Casper Oberschelp, David Martens, Alexandru-Gabriel Pielmus, Timo Tigges, Thomas Penzel, Reinhold Orglmeister
Wearable Multimodal Stethoscope Patch for Wireless Biosignal Acquisition and Long-Term Auscultation. EMBC (2019)
Dorien Herremans, David Martens, Kenneth Sörensen
Dance Hit Song Prediction. CoRR (2019)
Sofie De Cnudde, Julie Moeyersoms, Marija Stankova, Ellen Tobback, Vinayak Javaly, David Martens
What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance. J. Oper. Res. Soc. 70 (3) (2019)
Enric Junqué de Fortuny, David Martens, Foster J. Provost
Wallenius Bayes. Mach. Learn. 107 (6) (2018)
Jellis Vanhoeyveld, David Martens
Imbalanced classification in sparse and large behaviour datasets. Data Min. Knowl. Discov. 32 (1) (2018)
Wouter Verbeke, David Martens, Bart Baesens
RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. Appl. Soft Comput. 60 (2017)
Ellen Tobback, Tony Bellotti, Julie Moeyersoms, Marija Stankova, David Martens
Bankruptcy prediction for SMEs using relational data. Decis. Support Syst. 102 (2017)
Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens
Explaining Classification Models Built on High-Dimensional Sparse Data. CoRR (2016)
David Martens, Foster J. Provost, Jessica Clark, Enric Junqué de Fortuny
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. MIS Q. 40 (4) (2016)
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 (2015)
Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost
Iteratively refining SVMs using priors. IEEE BigData (2015)
Julie Moeyersoms, David Martens
Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decis. Support Syst. 72 (2015)
Foster J. Provost, David Martens, Alan Murray
Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. Inf. Syst. Res. 26 (2) (2015)
Dorien Herremans, Kenneth Sörensen, David Martens
Classification and Generation of Composer-Specific Music Using Global Feature Models and Variable Neighborhood Search. Comput. Music. J. 39 (3) (2015)
Sofie De Cnudde, David Martens
Loyal to your city? A data mining analysis of a public service loyalty program. Decis. Support Syst. 73 (2015)
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) (2015)
Enric Junqué de Fortuny, David Martens
Active Learning-Based Pedagogical Rule Extraction. IEEE Trans. Neural Networks Learn. Syst. 26 (11) (2015)
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) (2014)
Enric Junqué de Fortuny, Tom De Smedt, David Martens, Walter Daelemans
Evaluating and understanding text-based stock price prediction models. Inf. Process. Manag. 50 (2) (2014)
David Martens, Foster J. Provost
Explaining Data-Driven Document Classifications. MIS Q. 38 (1) (2014)
Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens
Corporate residence fraud detection. KDD (2014)
Wouter Verbeke, David Martens, Bart Baesens
Social network analysis for customer churn prediction. Appl. Soft Comput. 14 (2014)
Bart Minnaert, David Martens
A Comment on "Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization-Based Classifiers". IEEE Trans. Evol. Comput. 18 (5) (2014)
Enric Junqué de Fortuny, David Martens, Foster J. Provost
? Big Data 1 (4) (2013)
Bart Minnaert, David Martens
Towards a Particle Swarm Optimization-Based Regression Rule Miner. ICDM Workshops (2012)
Enric Junqué de Fortuny, David Martens
Active Learning Based Rule Extraction for Regression. ICDM Workshops (2012)
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) (2012)
Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Trans. Software Eng. 38 (2) (2012)
Enric Junqué de Fortuny, Tom De Smedt, David Martens, Walter Daelemans
Media coverage in times of political crisis: A text mining approach. Expert Syst. Appl. 39 (14) (2012)
David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens
Performance of classification models from a user perspective. Decis. Support Syst. 51 (4) (2011)
David Martens, Bart Baesens, Tom Fawcett
Editorial survey: swarm intelligence for data mining. Mach. Learn. 82 (1) (2011)
Bart Baesens, David Martens, Rudy Setiono, Jacek M. Zurada
Guest Editorial White Box Nonlinear Prediction Models. IEEE Trans. Neural Networks 22 (12) (2011)
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) (2011)
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) (2011)
Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens
Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38 (3) (2011)
Tony Van Gestel, Bart Baesens, David Martens
From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing 73 (16-18) (2010)
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) (2010)
Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
Software Effort Prediction Using Regression Rule Extraction from Neural Networks. ICTAI (2) (2010)
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) (2010)
David Martens, Bart Baesens, Tony Van Gestel
Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Trans. Knowl. Data Eng. 21 (2) (2009)
Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen
Including Domain Knowledge in Customer Churn Prediction Using AntMiner+. DMM@ICDM (2009)
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)
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) (2009)
Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens
Robust Process Discovery with Artificial Negative Events. J. Mach. Learn. Res. 10 (2009)
David Martens
Building acceptable classification models for financial engineering applications: thesis summary. SIGKDD Explor. 10 (2) (2008)
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) (2008)
David Martens, Liesbeth Bruynseels, Bart Baesens, Marleen Willekens, Jan Vanthienen
Predicting going concern opinion with data mining. Decis. Support Syst. 45 (4) (2008)
David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens
Classification With Ant Colony Optimization. IEEE Trans. Evol. Comput. 11 (5) (2007)
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)
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) (2007)
Johan Huysmans, David Martens, Bart Baesens, Jan Vanthienen, Tony Van Gestel
Country Corruption Analysis with Self Organizing Maps and Support Vector Machines. WISI (2006)
David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Christophe Mues, Jan Vanthienen
Ant-Based Approach to the Knowledge Fusion Problem. ANTS Workshop (2006)
Manu De Backer, Raf Haesen, David Martens, Bart Baesens
A Stigmergy Based Approach to Data Mining. Australian Conference on Artificial Intelligence (2005)