Justine Lootens
Ghent University
Statistical classificationMachine learningFinancial servicesAdaBoostArtificial intelligenceRandom forestCollaborative filteringClassifier chainsComputer scienceRecommender systemRelevance (information retrieval)
Publications 1
#1Matthias Bogaert (UGent: Ghent University)H-Index: 4
#2Justine Lootens (UGent: Ghent University)H-Index: 1
Last. Michel Ballings (UT: University of Tennessee)H-Index: 11
view all 4 authors...
Abstract The objective of this paper is to evaluate multi-label classification techniques and recommender systems for cross-sell purposes in the financial services sector. We carried out three analyses using data obtained from an international financial services provider. First, we tested four multi-label classification techniques, of which the two problem transformation methods were combined with several base classifiers. Second, we benchmarked the performance of five state-of-the-art recommend...
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