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Researcher
Bart Baesens
- Disciplines:Applied mathematics in specific fields, Artificial intelligence, Information sciences, Information systems, Cognitive science and intelligent systems, Management, Instructional sciences
Affiliations
- Information Systems Engineering Research Group (LIRIS), Leuven (Research group)
Member
From1 Nov 2005 → Today
Projects
1 - 10 of 34
- FraudPANDA -- Counteracting Fraud using Pro-Active (Network) Detection and AnalysisFrom1 Jan 2023 → TodayFunding: FWO Strategic Basic Research Grant
- Predictive Modelling and Big Data Analytics for Risk ManagementFrom15 Sep 2021 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Business Applications for Natural Language ProcessingFrom1 Sep 2021 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Applying deep learning on metadata data as a competitive acceleratorFrom1 Sep 2021 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- NOVEL SUSTAINABILITY-DRIVEN IOT PRESCRIPTIVE ANALYTICS FOR IMPROVING IRRIGATION PRACTICES IN FRUIT TREESFrom18 Jan 2021 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Data Science for Official Statistics.From1 Jan 2021 → TodayFunding: Department General Affairs and Finance
- Machine learning for fraud analyticsFrom27 Aug 2020 → TodayFunding: FWO fellowships
- Economic and Financial Data Infrastructure for Empirical Research in Economics and BusinessFrom1 May 2020 → TodayFunding: Own budget, for example: patrimony, inscription fees, gifts
- Economic and Financial Data Infrastructure for Empirical Research in Economics and BusinessFrom1 May 2020 → TodayFunding: FWO International research infrastructure (IRI)
- Statistical learning techniques with applications in finance and insuranceFrom2 Oct 2019 → 4 Jul 2023Funding: Own budget, for example: patrimony, inscription fees, gifts
Publications
1 - 10 of 233
- Statistical learning techniques with applications in finance and insurance(2023)
Authors: Emmanuel Jordy Menvouta Nkpwele, Tim Verdonck, Bart Baesens
- Optimizing the preventive maintenance frequency with causal machine learning(2023)
Authors: Toon Vanderschueren, Robert Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke
- Social network analytics for supervised fraud detection in insurance(2022)
Authors: Katrien Antonio, Bart Baesens, Tom Reynkens
Pages: 1872 - 1890 - Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies(2022)
Authors: Toon Vanderschueren, Bart Baesens, Tim Verdonck, Wouter Verbeke
Pages: 400 - 415 - A hierarchical mixture cure model with unobserved heterogeneity for credit risk(2022)
Authors: Gerda Claeskens, Bart Baesens
Pages: 39 - 55 - Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud(2022)
Authors: Bart Baesens, Wouter Verbeke, Tim Verdonck
Pages: 291 - 300 - Instance-dependent cost-sensitive learning: do we really need it?(2022)
Authors: Toon Vanderschueren, Tim Verdonck, Bart Baesens, Wouter Verbeke
Number of pages: 9 - Data engineering for fraud detection(2021)
Authors: Bart Baesens, Tim Verdonck
- robROSE: A robust approach for dealing with imbalanced data in fraud detection(2021)
Authors: Bart Baesens, Tim Verdonck
Pages: 841 - 861 - Expert-driven Trace Clustering with Instance-level Constraints(2021)
Authors: Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt