Selecting a multi-label classification method for an interactive system

Noureddine-Yassine Nair-Benrekia (1) , Pascale Kuntz (1) , Frank Meyer (2)

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

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).

Domaines: Apprentissage [cs.LG]

Noureddine-Yassine Nair-Benrekia, Pascale Kuntz, Frank Meyer. Selecting a multi-label classification method for an interactive system. Studies in Classification, Data Analysis, and Knowledge Organization, 2014, Data Analysis, Learning by Latent Structures, and Knowledge Discovery, pp.157-167. ⟨hal-00984294⟩