Research – ADM Research Group

Selected Publications

2026

Cash or Comfort? How LLMs Value Your Inconvenience

M Cedro, T Ichmoukhamedov, S Goethals, Y He, J Hinns, D Martens· Accepted at Communications of the ACM

arXiv ↗

2025

Tell me a story! Narrative-driven XAI with Large Language Models

D Martens, J Hinns, C Dams, M Vergouwen, T Evgeniou · Decision Support Systems

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All Publications

2026

Calculating and Visualizing Feature Importance Values for Counterfactual Explanations

B Meulemeester, RMB de Oliveira, Y He, D Martens · IEEE Access
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2026

Voluntary Tax Disclosures of Listed Companies in Europe

M Van Roy, A Jorissen, A Van de Vijver, D Martens · Accounting in Europe
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2026

Reranking individuals: The effect of fair classification within-groups

S Goethals, M Favier, T Calders · ACM Journal on Responsible Computing
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2026

Fairness principles across contexts: evaluating gender disparities of facts and opinions in large language models

S Goethals, L Rhue, A Sundararajan · AI and Ethics
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2026

Adapting Multiverse Analysis for Prediction: A Decision-Maker's Dashboard

F Ding, G Shmueli, T Greene, S Goethals · Navigating the Model Uncertainty and the Rashomon Effect
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2025

Monetization could corrupt algorithmic explanations

T Greene, S Goethals, D Martens, G Shmueli · AI & SOCIETY
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2025

One world, one opinion? The superstar effect in LLM responses

S Goethals, L Rhue · Proceedings of the 3rd Workshop on Cross-Cultural Considerations in NLP
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2025

The impact of cloaking digital footprints on user privacy and personalization

S Goethals, S Matz, F Provost, D Martens, Y Ramon · Big Data
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2025

Words reveal wants: How well can simple LLM-based AI agents replicate people's choices based on their social media posts

S Goethals, J Luther, S Matz · ACM Conference on User Modeling, Adaptation and Personalization
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2025

What If the Prompt Were Different? Counterfactual Explanations for the Characteristics of Generative Outputs

S Goethals, J Sedoc, F Provost · ACM Conference on User Modeling, Adaptation and Personalization
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2025

Tell me a story! Narrative-driven XAI with Large Language Models

D Martens, J Hinns, C Dams, M Vergouwen, T Evgeniou · Decision Support Systems
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2025

GraphXAIN: narratives to explain graph neural networks

M Cedro, D Martens · World Conference on Explainable Artificial Intelligence
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2025

Exposing Shortcuts in Image Classification by Aggregating Counterfactuals

J Hinns, D Martens · International Joint Conference on Computational Intelligence
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2024

PreCoF: counterfactual explanations for fairness

S Goethals, D Martens, T Calders · Machine Learning
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2024

Explainable AI for operational research: A defining framework, methods, applications, and a research agenda

KW De Bock, K Coussement, A De Caigny, R Słowiński, B Baesens et al. · European Journal of Operational Research
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2024

Disagreement amongst counterfactual explanations: how transparency can be misleading

D Brughmans, L Melis, D Martens · Top
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2024

Can metafeatures help improve explanations of prediction models when using behavioral and textual data?

Y Ramon, D Martens, T Evgeniou, S Praet · Machine Learning
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2024

A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data

RMB de Oliveira, K Sørensen, D Martens · European Journal of Operational Research
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2023

The privacy issue of counterfactual explanations: explanation linkage attacks

S Goethals, K Sørensen, D Martens · ACM Transactions on Intelligent Systems and Technology
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2023

Manipulation risks in Explainable AI: The implications of the disagreement problem

S Goethals, D Martens, T Evgeniou · Joint European Conference on Machine Learning and Knowledge Discovery
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2023

Explainability Methods to Detect and Measure Discrimination in Machine Learning Models

S Goethals, D Martens, T Calders · European Workshop on Algorithmic Fairness
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2023

AI can be both accurate and transparent

F Candelon, T Evgeniou, D Martens · Harvard Business Review
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2022

Explainable image classification with evidence counterfactual

T Vermeire, D Brughmans, S Goethals, RMB De Oliveira, D Martens · Pattern Analysis and Applications
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2022

The non-linear nature of the cost of comprehensibility

S Goethals, D Martens, T Evgeniou · Journal of Big Data
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2022

Data science ethics: Concepts, techniques, and cautionary tales

D Martens · Oxford University Press
Book ↗
2022

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

T Greene, D Martens, G Shmueli · Nature Machine Intelligence
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2021

A framework and benchmarking study for counterfactual generating methods on tabular data

RMB de Oliveira, D Martens · Applied Sciences
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2021

How to choose an explainability method? Towards a methodical implementation of XAI in practice

T Vermeire, T Laugel, X Renard, D Martens, M Detyniecki · Joint European Conference on Machine Learning and Knowledge Discovery
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2021

Explainable AI for psychological profiling from behavioral data: An application to big five personality predictions from financial transaction records

Y Ramon, RA Farrokhnia, SC Matz, D Martens · Information
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2021

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

S Praet, D Martens, P Van Aelst · Online Social Networks and Media
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2021

Predictive modeling to study lifestyle politics with Facebook likes

S Praet, P Van Aelst, P van Erkel, S Van der Veeken, D Martens · EPJ Data Science
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2021

Node classification over bipartite graphs through projection

M Stankova, S Praet, D Martens, F Provost · Machine Learning
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2020

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

Y Ramon, D Martens, F Provost, T Evgeniou · Advances in Data Analysis and Classification
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2020

Value-added tax fraud detection with scalable anomaly detection techniques

J Vanhoeyveld, D Martens, B Peeters · Applied Soft Computing
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2020

Efficient parcel delivery by predicting customers' locations

S Praet, D Martens · Decision Sciences
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2020

A benchmarking study of classification techniques for behavioral data

S De Cnudde, D Martens, T Evgeniou, F Provost · International Journal of Data Science and Analytics
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2019

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

S De Cnudde, J Moeyersoms, M Stankova, E Tobback, V Javaly et al. · Journal of the Operational Research Society
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2019

Retail credit scoring using fine-grained payment data

E Tobback, D Martens · Journal of the Royal Statistical Society: Series A
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2019

Customs fraud detection: Assessing the value of behavioural and high-cardinality data under the imbalanced learning issue

J Vanhoeyveld, D Martens, B Peeters · Pattern Analysis and Applications
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2019

Deep learning on big, sparse, behavioral data

S De Cnudde, Y Ramon, D Martens, F Provost · Big Data
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2018

Belgian economic policy uncertainty index: Improvement through text mining

E Tobback, H Naudts, W Daelemans, EJ de Fortuny, D Martens · International Journal of Forecasting
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2018

Imbalanced classification in sparse and large behaviour datasets

J Vanhoeyveld, D Martens · Data Mining and Knowledge Discovery
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2018

Wallenius Bayes

E Junqué de Fortuny, D Martens, F Provost · Machine Learning
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2017

Bankruptcy prediction for SMEs using relational data

E Tobback, T Bellotti, J Moeyersoms, M Stankova, D Martens · Decision Support Systems
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2017

Between hawks and doves: measuring central bank communication

E Tobback, S Nardelli, D Martens · ECB Working Paper
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2017

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

W Verbeke, D Martens, B Baesens · Applied Soft Computing
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2016

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics

D Martens, EJ de Fortuny, J Clark, F Provost · MIS Quarterly
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2016

Explaining Classification Models Built on High-Dimensional Sparse Data

J Moeyersoms, B d'Alessandro, F Provost, D Martens · Proceedings of ICML
arXiv ↗
2015

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

J Moeyersoms, D Martens · Decision Support Systems
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2015

Comprehensible software fault and effort prediction: A data mining approach

J Moeyersoms, EJ de Fortuny, K Dejaeger, B Baesens, D Martens · Journal of Systems and Software
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2015

Finding similar mobile consumers with a privacy-friendly geosocial design

F Provost, D Martens, A Murray · Information Systems Research
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2015

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

S De Cnudde, D Martens · Decision Support Systems
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2015

Active learning-based pedagogical rule extraction

EJ De Fortuny, D Martens · IEEE Transactions on Neural Networks and Learning Systems
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2015

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

B Minnaert, D Martens, M De Backer, B Baesens · Data Mining and Knowledge Discovery
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2014

Explaining data-driven document classifications

D Martens, F Provost · MIS Quarterly
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2014

Evaluating and understanding text-based stock price prediction models

EJ De Fortuny, T De Smedt, D Martens, W Daelemans · Information Processing & Management
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2014

Social network analysis for customer churn prediction

W Verbeke, D Martens, B Baesens · Applied Soft Computing
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2014

Corporate residence fraud detection

E Junqué de Fortuny, M Stankova, J Moeyersoms, B Minnaert, F Provost et al. · Proceedings of the 20th ACM SIGKDD International Conference
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2014

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

E Tobback, D Martens, T Van Gestel, B Baesens · Journal of the Operational Research Society
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2013

Predictive Modeling With Big Data: Is Bigger Really Better?

E Junqué de Fortuny, D Martens, F Provost · Big Data
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2013

A novel credit rating migration modeling approach using macroeconomic indicators

K Berteloot, W Verbeke, G Castermans, T Van Gestel, D Martens et al. · Journal of Forecasting
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2012

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

W Verbeke, K Dejaeger, D Martens, J Hur, B Baesens · European Journal of Operational Research
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2012

Benchmarking regression algorithms for loss given default modeling

G Loterman, I Brown, D Martens, C Mues, B Baesens · International Journal of Forecasting
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2012

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

EJ de Fortuny, T De Smedt, D Martens, W Daelemans · Expert Systems with Applications
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2011

Building comprehensible customer churn prediction models with advanced rule induction techniques

W Verbeke, D Martens, C Mues, B Baesens · Expert Systems with Applications
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2011

Editorial survey: swarm intelligence for data mining

D Martens, B Baesens, T Fawcett · Machine Learning
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2011

Data mining techniques for software effort estimation: a comparative study

K Dejaeger, W Verbeke, D Martens, B Baesens · IEEE Transactions on Software Engineering
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2011

Performance of classification models from a user perspective

D Martens, J Vanthienen, W Verbeke, B Baesens · Decision Support Systems
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2011

Identifying financially successful start-up profiles with data mining

D Martens, C Vanhoutte, S De Winne, B Baesens, L Sels, C Mues · Expert Systems with Applications
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2010

An overview and framework for PD backtesting and benchmarking

G Castermans, D Martens, TV Gestel, B Hamers, B Baesens · Journal of the Operational Research Society
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2010

Credit rating prediction using ant colony optimization

D Martens, T Van Gestel, M De Backer, R Haesen, J Vanthienen et al. · Journal of the Operational Research Society
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2010

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

T Van Gestel, B Baesens, D Martens · Neurocomputing
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2009

Robust Process Discovery with Artificial Negative Events

S Goedertier, D Martens, J Vanthienen, B Baesens · Journal of Machine Learning Research
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2009

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

B Baesens, C Mues, D Martens, J Vanthienen · Journal of the Operational Research Society
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2009

Inferring comprehensible business/ICT alignment rules

B Cumps, D Martens, M De Backer, R Haesen, S Viaene, G Dedene et al. · Information & Management
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2008

Decompositional rule extraction from support vector machines by active learning

D Martens, BB Baesens, T Van Gestel · IEEE Transactions on Knowledge and Data Engineering
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2008

Predicting going concern opinion with data mining

D Martens, L Bruynseels, B Baesens, M Willekens, J Vanthienen · Decision Support Systems
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2008

Mining software repositories for comprehensible software fault prediction models

O Vandecruys, D Martens, B Baesens, C Mues, M De Backer, R Haesen · Journal of Systems and Software
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2007

Comprehensible credit scoring models using rule extraction from support vector machines

D Martens, B Baesens, T Van Gestel, J Vanthienen · European Journal of Operational Research
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2007

Classification with ant colony optimization

D Martens, M De Backer, R Haesen, J Vanthienen, M Snoeck, B Baesens · IEEE Transactions on Evolutionary Computation
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2007

Forecasting and analyzing insurance companies' ratings

T Van Gestel, D Martens, B Baesens, D Feremans, J Huysmans et al. · International Journal of Forecasting
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2006

Ant-based approach to the knowledge fusion problem

D Martens, M De Backer, R Haesen, B Baesens, C Mues, J Vanthienen · International Workshop on Ant Colony Optimization and Swarm Intelligence
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2006

Country corruption analysis with self organizing maps and support vector machines

J Huysmans, D Martens, B Baesens, J Vanthienen, T Van Gestel · International Workshop on Intelligence and Security Informatics
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2005

New trends in data mining

J Huysmans, B Baesens, D Martens, K Denys, J Vanthienen · Tijdschrift voor Economie en Management
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2005

Benchmarking state-of-the-art classification techniques for credit scoring

D Martens, B Baesens, T Van Gestel, J Vanthienen · International Symposium on Applied Stochastic Models and Data Analysis