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1 LINA - Laboratoire d-Informatique de Nantes Atlantique 2 Orange Labs Lannion

Abstract : Interactive classification-based systems engage users to coach learning algorithms to take into account their own individual preferences. However most of the recent interactive systems limit the users to a single-label classification, which may be not expressive enough in some organization tasks such as film classification, where a multi-label scheme is required. The objective of this paper is to compare the behaviors of 12 multi-label classification methods in an interactive framework where -good- predictions must be produced in a very short time from a very small set of multi-label training examples. Experimentations highlight important performance differences for 4 complementary evaluation measures Log-Loss, Ranking-Loss, Learning and Prediction Times. The best results are obtained for Multi-label k Nearest Neighbours ML-kNN, Ensemble of Classifier Chains ECC and Ensemble of Binary Relevance EBR.

Author: Noureddine-Yassine Nair-Benrekia - Pascale Kuntz - Frank Meyer -



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