David Martens

According to our database, David Martens authored at least 90 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics.

Sofie Goethals, Toon Calders, David Martens

CoRR, 2024

2023

The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks.

Sofie Goethals, Kenneth Sörensen, David Martens

ACM Trans. Intell. Syst. Technol., October, 2023

Erratum to.

Stiene Praet, David Martens, Peter Van Aelst

Online Soc. Networks Media, May, 2023

How sustainable is "common" data science in terms of power consumption?

Bjorge Meulemeester, David Martens

Sustain. Comput. Informatics Syst., April, 2023

The Impact of Cloaking Digital Footprints on User Privacy and Personalization.

Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens

CoRR, 2023

Tell Me a Story! Narrative-Driven XAI with Large Language Models.

David Martens, Camille Dams, James Hinns, Mark Vergouwen

CoRR, 2023

Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem.

Sofie Goethals, David Martens, Theodoros Evgeniou

CoRR, 2023

Calculating and Visualizing Counterfactual Feature Importance Values.

Bjorge Meulemeester, Raphael Mazzine Barbosa de Oliveira, David Martens

CoRR, 2023

Unveiling the Potential of Counterfactuals Explanations in Employability.

Raphael Mazzine Barbosa de Oliveira, Sofie Goethals, Dieter Brughmans, David Martens

CoRR, 2023

Disagreement amongst counterfactual explanations: How transparency can be deceptive.

Dieter Brughmans, Lissa Melis, David Martens

CoRR, 2023

Monetizing Explainable AI: A Double-edged Sword.

Travis Greene, Sofie Goethals, David Martens, Galit Shmueli

CoRR, 2023

Explainability Methods to Detect and Measure Discrimination in Machine Learning Models.

Sofie Goethals, David Martens, Toon Calders

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

2022

Explainable image classification with evidence counterfactual.

Tom Vermeire, Dieter Brughmans, Sofie Goethals, Raphael Mazzine Barbossa de Oliveira, David Martens

Pattern Anal. Appl., 2022

Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms.

Travis Greene, David Martens, Galit Shmueli

Nat. Mach. Intell., 2022

The non-linear nature of the cost of comprehensibility.

Sofie Goethals, David Martens, Theodoros Evgeniou

J. Big Data, 2022

2021

Patterns of democracy? Social network analysis of parliamentary Twitter networks in 12 countries.

Stiene Praet, David Martens, Peter Van Aelst

Online Soc. Networks Media, 2021

Node classification over bipartite graphs through projection.

Marija Stankova, Stiene Praet, David Martens, Foster J. Provost

Mach. Learn., 2021

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records.

Yanou Ramon, R. A. Farrokhnia, Sandra C. Matz, David Martens

Inf., 2021

Predictive modeling to study lifestyle politics with Facebook likes.

Stiene Praet, Peter Van Aelst, Patrick van Erkel, Stephan Van der Veeken, David Martens

EPJ Data Sci., 2021

Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data.

Yanou Ramon, Sandra C. Matz, R. A. Farrokhnia, David Martens

CoRR, 2021

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data.

Raphael Mazzine, David Martens

CoRR, 2021

Understanding Consumer Preferences for Explanations Generated by XAI Algorithms.

Yanou Ramon, Tom Vermeire, Olivier Toubia, David Martens, Theodoros Evgeniou

CoRR, 2021

NICE: An Algorithm for Nearest Instance Counterfactual Explanations.

Dieter Brughmans, David Martens

CoRR, 2021

How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice.

Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

2020

Customs fraud detection.

Jellis Vanhoeyveld, David Martens, Bruno Peeters

Pattern Anal. Appl., 2020

A benchmarking study of classification techniques for behavioral data.

Sofie De Cnudde, David Martens, Theodoros Evgeniou, Foster J. Provost

Int. J. Data Sci. Anal., 2020

Efficient Parcel Delivery by Predicting Customers' Locations.

Stiene Praet, David Martens

Decis. Sci., 2020

Explainable Image Classification with Evidence Counterfactual.

Tom Vermeire, David Martens

CoRR, 2020

Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data.

Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet

CoRR, 2020

Value-added tax fraud detection with scalable anomaly detection techniques.

Jellis Vanhoeyveld, David Martens, Bruno Peeters

Appl. Soft Comput., 2020

A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C.

Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou

Adv. Data Anal. Classif., 2020

2019

What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance.

Sofie De Cnudde, Julie Moeyersoms, Marija Stankova, Ellen Tobback, Vinayak Javaly, David Martens

J. Oper. Res. Soc., 2019

Counterfactual Explanation Algorithms for Behavioral and Textual Data.

Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou

CoRR, 2019

Dance Hit Song Prediction.

Dorien Herremans, David Martens, Kenneth Sörensen

CoRR, 2019

Deep Learning on Big, Sparse, Behavioral Data.

Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost

Big Data, 2019

Wearable Multimodal Stethoscope Patch for Wireless Biosignal Acquisition and Long-Term Auscultation.

Michael Klum, Fabian Leib, Casper Oberschelp, David Martens, Alexandru-Gabriel Pielmus, Timo Tigges, Thomas Penzel, Reinhold Orglmeister

Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

2018

Wallenius Bayes.

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

Mach. Learn., 2018

Imbalanced classification in sparse and large behaviour datasets.

Jellis Vanhoeyveld, David Martens

Data Min. Knowl. Discov., 2018

2017

Bankruptcy prediction for SMEs using relational data.

Ellen Tobback, Tony Bellotti, Julie Moeyersoms, Marija Stankova, David Martens

Decis. Support Syst., 2017

RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints.

Wouter Verbeke, David Martens, Bart Baesens

Appl. Soft Comput., 2017

2016

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics.

David Martens, Foster J. Provost, Jessica Clark, Enric Junqué de Fortuny

MIS Q., 2016

Explaining Classification Models Built on High-Dimensional Sparse Data.

Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens

CoRR, 2016

Composer Classification Models for Music-Theory Building.

Dorien Herremans, David Martens, Kenneth Sörensen

Proceedings of the Computational Music Analysis, 2016

2015

Active Learning-Based Pedagogical Rule Extraction.

Enric Junqué de Fortuny, David Martens

IEEE Trans. Neural Networks Learn. Syst., 2015

Comprehensible software fault and effort prediction: A data mining approach.

Julie Moeyersoms, Enric Junqué de Fortuny, Karel Dejaeger, Bart Baesens, David Martens

J. Syst. Softw., 2015

Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design.

Foster J. Provost, David Martens, Alan Murray

Inf. Syst. Res., 2015

Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector.

Julie Moeyersoms, David Martens

Decis. Support Syst., 2015

Loyal to your city? A data mining analysis of a public service loyalty program.

Sofie De Cnudde, David Martens

Decis. Support Syst., 2015

To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms.

Bart Minnaert, David Martens, Manu De Backer, Bart Baesens

Data Min. Knowl. Discov., 2015

Classification and Generation of Composer-Specific Music Using Global Feature Models and Variable Neighborhood Search.

Dorien Herremans, Kenneth Sörensen, David Martens

Comput. Music. J., 2015

Iteratively refining SVMs using priors.

Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

2014

A Comment on "Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization-Based Classifiers".

Bart Minnaert, David Martens

IEEE Trans. Evol. Comput., 2014

Explaining Data-Driven Document Classifications.

David Martens, Foster J. Provost

MIS Q., 2014

Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state.

Ellen Tobback, David Martens, Tony Van Gestel, Bart Baesens

J. Oper. Res. Soc., 2014

Evaluating and understanding text-based stock price prediction models.

Enric Junqué de Fortuny, Tom De Smedt, David Martens, Walter Daelemans

Inf. Process. Manag., 2014

Social network analysis for customer churn prediction.

Wouter Verbeke, David Martens, Bart Baesens

Appl. Soft Comput., 2014

Corporate residence fraud detection.

Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2013

Predictive Modeling With Big Data: <i>Is Bigger Really Better</i>?

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

Big Data, 2013

2012

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

Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens

IEEE Trans. Software Eng., 2012

Media coverage in times of political crisis: A text mining approach.

Enric Junqué de Fortuny, Tom De Smedt, David Martens, Walter Daelemans

Expert Syst. Appl., 2012

New insights into churn prediction in the telecommunication sector: A profit driven data mining approach.

Wouter Verbeke, Karel Dejaeger, David Martens, Joon Hur, Bart Baesens

Eur. J. Oper. Res., 2012

Towards a Particle Swarm Optimization-Based Regression Rule Miner.

Bart Minnaert, David Martens

Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012

Active Learning Based Rule Extraction for Regression.

Enric Junqué de Fortuny, David Martens

Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012

2011

Guest Editorial White Box Nonlinear Prediction Models.

Bart Baesens, David Martens, Rudy Setiono, Jacek M. Zurada

IEEE Trans. Neural Networks, 2011

Editorial survey: swarm intelligence for data mining.

David Martens, Bart Baesens, Tom Fawcett

Mach. Learn., 2011

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

Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens

Expert Syst. Appl., 2011

Identifying financially successful start-up profiles with data mining.

David Martens, Christine Vanhoutte, Sophie De Winne, Bart Baesens, Luc Sels, Christophe Mues

Expert Syst. Appl., 2011

Performance of classification models from a user perspective.

David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens

Decis. Support Syst., 2011

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

Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart Baesens

Appl. Soft Comput., 2011

2010

Building Acceptable Classification Models.

David Martens, Bart Baesens

Proceedings of the Data Mining - Special Issue in Annals of Information Systems, 2010

Credit rating prediction using Ant Colony Optimization.

David Martens, Tony Van Gestel, Manu De Backer, Raf Haesen, Jan Vanthienen, Bart Baesens

J. Oper. Res. Soc., 2010

An overview and framework for PD backtesting and benchmarking.

G. Castermans, David Martens, Tony Van Gestel, Bart Hamers, Bart Baesens

J. Oper. Res. Soc., 2010

From linear to non-linear kernel based classifiers for bankruptcy prediction.

Tony Van Gestel, Bart Baesens, David Martens

Neurocomputing, 2010

Software Effort Prediction Using Regression Rule Extraction from Neural Networks.

Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens

Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010

2009

Decompositional Rule Extraction from Support Vector Machines by Active Learning.

David Martens, Bart Baesens, Tony Van Gestel

IEEE Trans. Knowl. Data Eng., 2009

50 years of data mining and OR: upcoming trends and challenges.

Bart Baesens, Christophe Mues, David Martens, Jan Vanthienen

J. Oper. Res. Soc., 2009

Robust Process Discovery with Artificial Negative Events.

Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens

J. Mach. Learn. Res., 2009

Inferring comprehensible business/ICT alignment rules.

Bjorn Cumps, David Martens, Manu De Backer, Raf Haesen, Stijn Viaene, Guido Dedene, Bart Baesens, Monique Snoeck

Inf. Manag., 2009

Including Domain Knowledge in Customer Churn Prediction Using AntMiner+.

Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen

Proceedings of the Advances in Data Mining in Marketing. 9th Industrial Conference, 2009

2008

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

David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens

Proceedings of the Rule Extraction from Support Vector Machines, 2008

Building acceptable classification models for financial engineering applications: thesis summary.

David Martens

SIGKDD Explor., 2008

Mining software repositories for comprehensible software fault prediction models.

Olivier Vandecruys, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, Raf Haesen

J. Syst. Softw., 2008

Predicting going concern opinion with data mining.

David Martens, Liesbeth Bruynseels, Bart Baesens, Marleen Willekens, Jan Vanthienen

Decis. Support Syst., 2008

2007

Classification With Ant Colony Optimization.

David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens

IEEE Trans. Evol. Comput., 2007

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

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

Eur. J. Oper. Res., 2007

Process Mining as First-Order Classification Learning on Logs with Negative Events.

Stijn Goedertier, David Martens, Bart Baesens, Raf Haesen, Jan Vanthienen

Proceedings of the Business Process Management Workshops, 2007

2006

Ants Constructing Rule-Based Classifiers.

David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Tom Holvoet

Proceedings of the Swarm Intelligence in Data Mining, 2006

Country Corruption Analysis with Self Organizing Maps and Support Vector Machines.

Johan Huysmans, David Martens, Bart Baesens, Jan Vanthienen, Tony Van Gestel

Proceedings of the Intelligence and Security Informatics, International Workshop, 2006

Ant-Based Approach to the Knowledge Fusion Problem.

David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Christophe Mues, Jan Vanthienen

Proceedings of the Ant Colony Optimization and Swarm Intelligence, 2006

2005

A Stigmergy Based Approach to Data Mining.

Manu De Backer, Raf Haesen, David Martens, Bart Baesens

Proceedings of the AI 2005: Advances in Artificial Intelligence, 2005