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David Martens
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2020 – today
- 2024
- [j61]Dieter Brughmans
, Pieter Leyman, David Martens:
NICE: an algorithm for nearest instance counterfactual explanations. Data Min. Knowl. Discov. 38(5): 2665-2703 (2024) - [j60]Koen W. De Bock
Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. Eur. J. Oper. Res. 317(2): 249-272 (2024) - [j59]Raphael Mazzine Barbosa de Oliveira
A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data. Eur. J. Oper. Res. 317(2): 286-302 (2024) - [j58]Sofie Goethals
PreCoF: counterfactual explanations for fairness. Mach. Learn. 113(5): 3111-3142 (2024) - [j57]Yanou Ramon
Can metafeatures help improve explanations of prediction models when using behavioral and textual data? Mach. Learn. 113(7): 4245-4284 (2024) - [i21]Sofie Goethals, Toon Calders, David Martens:
Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics. CoRR abs/2401.13391 (2024) - [i20]James Hinns
Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables. CoRR abs/2405.15661 (2024) - 2023
- [j56]Stiene Praet, David Martens, Peter Van Aelst:
Erratum to. Online Soc. Networks Media 34-35: 100246 (2023) - [j55]Bjorge Meulemeester
How sustainable is "common" data science in terms of power consumption? Sustain. Comput. Informatics Syst. 38: 100864 (2023) - [j54]Sofie Goethals
The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks. ACM Trans. Intell. Syst. Technol. 14(5): 83:1-83:24 (2023) - [c13]Sofie Goethals, David Martens, Toon Calders:
Explainability Methods to Detect and Measure Discrimination in Machine Learning Models. EWAF 2023 - [i19]Travis Greene, Sofie Goethals, David Martens, Galit Shmueli:
Monetizing Explainable AI: A Double-edged Sword. CoRR abs/2304.06483 (2023) - [i18]Dieter Brughmans, Lissa Melis, David Martens:
Disagreement amongst counterfactual explanations: How transparency can be deceptive. CoRR abs/2304.12667 (2023) - [i17]Raphael Mazzine Barbosa de Oliveira, Sofie Goethals, Dieter Brughmans, David Martens:
Unveiling the Potential of Counterfactuals Explanations in Employability. CoRR abs/2305.10069 (2023) - [i16]Bjorge Meulemeester, Raphael Mazzine Barbosa de Oliveira, David Martens:
Calculating and Visualizing Counterfactual Feature Importance Values. CoRR abs/2306.06506 (2023) - [i15]Sofie Goethals, David Martens, Theodoros Evgeniou:
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. CoRR abs/2306.13885 (2023) - [i14]David Martens, Camille Dams, James Hinns
Tell Me a Story! Narrative-Driven XAI with Large Language Models. CoRR abs/2309.17057 (2023) - [i13]Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens:
The Impact of Cloaking Digital Footprints on User Privacy and Personalization. CoRR abs/2312.15000 (2023) - 2022
- [j53]Sofie Goethals
The non-linear nature of the cost of comprehensibility. J. Big Data 9(1): 30 (2022) - [j52]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): 323-330 (2022) - [j51]Tom Vermeire, Dieter Brughmans, Sofie Goethals
Explainable image classification with evidence counterfactual. Pattern Anal. Appl. 25(2): 315-335 (2022) - [i12]Bjorge Meulemeester, David Martens:
How sustainable is "common" data science in terms of power consumption? CoRR abs/2207.01934 (2022) - [i11]Sofie Goethals, Kenneth Sörensen, David Martens:
The privacy issue of counterfactual explanations: explanation linkage attacks. CoRR abs/2210.12051 (2022) - 2021
- [j50]Stiene Praet
Predictive modeling to study lifestyle politics with Facebook likes. EPJ Data Sci. 10(1): 50 (2021) - [j49]Yanou Ramon
Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records. Inf. 12(12): 518 (2021) - [j48]Marija Stankova, Stiene Praet
Node classification over bipartite graphs through projection. Mach. Learn. 110(1): 37-87 (2021) - [j47]Stiene Praet
Patterns of democracy? Social network analysis of parliamentary Twitter networks in 12 countries. Online Soc. Networks Media 24: 100154 (2021) - [c12]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: 521-533 - [i10]Dieter Brughmans, David Martens:
NICE: An Algorithm for Nearest Instance Counterfactual Explanations. CoRR abs/2104.07411 (2021) - [i9]Yanou Ramon, Tom Vermeire, Olivier Toubia, David Martens, Theodoros Evgeniou:
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms. CoRR abs/2107.02624 (2021) - [i8]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 abs/2107.04427 (2021) - [i7]Raphael Mazzine, David Martens:
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data. CoRR abs/2107.04680 (2021) - [i6]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 abs/2111.06908 (2021) - 2020
- [j46]Yanou Ramon
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): 801-819 (2020) - [j45]Jellis Vanhoeyveld
Value-added tax fraud detection with scalable anomaly detection techniques. Appl. Soft Comput. 86 (2020) - [j44]Stiene Praet
Efficient Parcel Delivery by Predicting Customers' Locations. Decis. Sci. 51(5): 1202-1231 (2020) - [j43]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): 131-173 (2020) - [j42]Jellis Vanhoeyveld
Customs fraud detection. Pattern Anal. Appl. 23(3): 1457-1477 (2020) - [i5]Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet:
Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data. CoRR abs/2003.04792 (2020) - [i4]Tom Vermeire, David Martens:
Explainable Image Classification with Evidence Counterfactual. CoRR abs/2004.07511 (2020)
2010 – 2019
- 2019
- [j41]Sofie De Cnudde, Yanou Ramon
Deep Learning on Big, Sparse, Behavioral Data. Big Data 7(4): 286-307 (2019) - [j40]Sofie De Cnudde, Julie Moeyersoms
What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance. J. Oper. Res. Soc. 70(3): 353-363 (2019) - [c11]Michael Klum, Fabian Leib, Casper Oberschelp, David Martens, Alexandru-Gabriel Pielmus, Timo Tigges, Thomas Penzel
Wearable Multimodal Stethoscope Patch for Wireless Biosignal Acquisition and Long-Term Auscultation. EMBC 2019: 5781-5785 - [i3]Dorien Herremans
Dance Hit Song Prediction. CoRR abs/1905.08076 (2019) - [i2]Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou:
Counterfactual Explanation Algorithms for Behavioral and Textual Data. CoRR abs/1912.01819 (2019) - 2018
- [j39]Jellis Vanhoeyveld
Imbalanced classification in sparse and large behaviour datasets. Data Min. Knowl. Discov. 32(1): 25-82 (2018) - [j38]Enric Junqué de Fortuny, David Martens, Foster J. Provost:
Wallenius Bayes. Mach. Learn. 107(6): 1013-1037 (2018) - 2017
- [j37]Wouter Verbeke
RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. Appl. Soft Comput. 60: 858-873 (2017) - [j36]Ellen Tobback, Tony Bellotti
Bankruptcy prediction for SMEs using relational data. Decis. Support Syst. 102: 69-81 (2017) - 2016
- [j35]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): 869-888 (2016) - [p4]Dorien Herremans
Composer Classification Models for Music-Theory Building. Computational Music Analysis 2016: 369-392 - [i1]Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens:
Explaining Classification Models Built on High-Dimensional Sparse Data. CoRR abs/1607.06280 (2016) - 2015
- [j34]Dorien Herremans
Classification and Generation of Composer-Specific Music Using Global Feature Models and Variable Neighborhood Search. Comput. Music. J. 39(3): 71-91 (2015) - [j33]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): 237-272 (2015) - [j32]Julie Moeyersoms
Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decis. Support Syst. 72: 72-81 (2015) - [j31]Sofie De Cnudde, David Martens:
Loyal to your city? A data mining analysis of a public service loyalty program. Decis. Support Syst. 73: 74-84 (2015) - [j30]Foster J. Provost, David Martens, Alan Murray:
Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. Inf. Syst. Res. 26(2): 243-265 (2015) - [j29]Julie Moeyersoms
Comprehensible software fault and effort prediction: A data mining approach. J. Syst. Softw. 100: 80-90 (2015) - [j28]Enric Junqué de Fortuny, David Martens:
Active Learning-Based Pedagogical Rule Extraction. IEEE Trans. Neural Networks Learn. Syst. 26(11): 2664-2677 (2015) - [c10]Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost:
Iteratively refining SVMs using priors. IEEE BigData 2015: 46-52 - 2014
- [j27]Wouter Verbeke
Social network analysis for customer churn prediction. Appl. Soft Comput. 14: 431-446 (2014) - [j26]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): 426-441 (2014) - [j25]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): 376-392 (2014) - [j24]David Martens, Foster J. Provost:
Explaining Data-Driven Document Classifications. MIS Q. 38(1): 73-99 (2014) - [j23]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): 790-791 (2014) - [c9]Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms
Corporate residence fraud detection. KDD 2014: 1650-1659 - 2013
- [j22]Enric Junqué de Fortuny, David Martens, Foster J. Provost:
Predictive Modeling With Big Data: Is Bigger Really Better? Big Data 1(4): 215-226 (2013) - 2012
- [j21]Wouter Verbeke
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. Eur. J. Oper. Res. 218(1): 211-229 (2012) - [j20]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): 11616-11622 (2012) - [j19]Karel Dejaeger, Wouter Verbeke
Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Trans. Software Eng. 38(2): 375-397 (2012) - [c8]Enric Junqué de Fortuny, David Martens:
Active Learning Based Rule Extraction for Regression. ICDM Workshops 2012: 926-933 - [c7]Bart Minnaert, David Martens:
Towards a Particle Swarm Optimization-Based Regression Rule Miner. ICDM Workshops 2012: 961-963 - 2011
- [j18]Stijn Goedertier, Jochen De Weerdt
Process discovery in event logs: An application in the telecom industry. Appl. Soft Comput. 11(2): 1697-1710 (2011) - [j17]David Martens, Jan Vanthienen
Performance of classification models from a user perspective. Decis. Support Syst. 51(4): 782-793 (2011) - [j16]Wouter Verbeke
Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38(3): 2354-2364 (2011) - [j15]David Martens, Christine Vanhoutte
Identifying financially successful start-up profiles with data mining. Expert Syst. Appl. 38(5): 5794-5800 (2011) - [j14]David Martens, Bart Baesens, Tom Fawcett
Editorial survey: swarm intelligence for data mining. Mach. Learn. 82(1): 1-42 (2011) - [j13]Bart Baesens, David Martens, Rudy Setiono, Jacek M. Zurada:
Guest Editorial White Box Nonlinear Prediction Models. IEEE Trans. Neural Networks 22(12): 2406-2408 (2011) - 2010
- [j12]Tony Van Gestel, Bart Baesens, David Martens:
From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing 73(16-18): 2955-2970 (2010) - [j11]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): 359-373 (2010) - [j10]David Martens, Tony Van Gestel, Manu De Backer, Raf Haesen, Jan Vanthienen
Credit rating prediction using Ant Colony Optimization. J. Oper. Res. Soc. 61(4): 561-573 (2010) - [c6]Rudy Setiono, Karel Dejaeger, Wouter Verbeke
Software Effort Prediction Using Regression Rule Extraction from Neural Networks. ICTAI (2) 2010: 45-52 - [p3]David Martens, Bart Baesens:
Building Acceptable Classification Models. Data Mining 2010: 53-74
2000 – 2009
- 2009
- [j9]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): 116-124 (2009) - [j8]Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens:
Robust Process Discovery with Artificial Negative Events. J. Mach. Learn. Res. 10: 1305-1340 (2009) - [j7]Bart Baesens, Christophe Mues
50 years of data mining and OR: upcoming trends and challenges. J. Oper. Res. Soc. 60(S1) (2009) - [j6]David Martens, Bart Baesens, Tony Van Gestel:
Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Trans. Knowl. Data Eng. 21(2): 178-191 (2009) - [c5]Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen:
Including Domain Knowledge in Customer Churn Prediction Using AntMiner+. DMM@ICDM 2009: 10-21 - 2008
- [j5]David Martens, Liesbeth Bruynseels
Predicting going concern opinion with data mining. Decis. Support Syst. 45(4): 765-777 (2008) - [j4]Olivier Vandecruys, David Martens, Bart Baesens, Christophe Mues
Mining software repositories for comprehensible software fault prediction models. J. Syst. Softw. 81(5): 823-839 (2008) - [j3]David Martens:
Building acceptable classification models for financial engineering applications: thesis summary. SIGKDD Explor. 10(2): 30-31 (2008) - [p2]David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. Rule Extraction from Support Vector Machines 2008: 33-63 - 2007
- [j2]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): 1466-1476 (2007) - [j1]David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen
Classification With Ant Colony Optimization. IEEE Trans. Evol. Comput. 11(5): 651-665 (2007) - [c4]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: 42-53 - 2006
- [c3]David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Christophe Mues
Ant-Based Approach to the Knowledge Fusion Problem. ANTS Workshop 2006: 84-95 - [c2]Johan Huysmans, David Martens, Bart Baesens, Jan Vanthienen
Country Corruption Analysis with Self Organizing Maps and Support Vector Machines. WISI 2006: 103-114 - [p1]David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Tom Holvoet
Ants Constructing Rule-Based Classifiers. Swarm Intelligence in Data Mining 2006: 21-43 - 2005
- [c1]Manu De Backer, Raf Haesen, David Martens, Bart Baesens:
A Stigmergy Based Approach to Data Mining. Australian Conference on Artificial Intelligence 2005: 975-978
Coauthor Index
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last updated on 2024-12-10 20:49 CET by the dblp team
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