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

Abstract : Interactive classification aims at introducing user preferences in the learning process to produce individualized outcomes more adapted to each user’s behaviour than the fully automatic approaches. The current interactive classification systems generally adopt a singlelabel classification paradigm that constrains items to span one label at a time and consequently limit the user’s expressiveness while he-she interacts with data that are inherently multi-label. Moreover, the experimental evaluations are mainly subjective and closely depend on the targeted use cases and the interface characteristics. This paper presents the first extensive study of the impact of the interactivity constraints on the performances of a large set of twelve well-established multi-label learning methods. We restrict ourselves to the evaluation of the classifier predictive and time-computation performances while the number of training examples regularly increases and we focus on the beginning of the classification task where few examples are available. The classifier performances are evaluated with an experimental protocol independent of any implementation environment on a set of twelve multi-label benchmarks of various sizes from different domains. Our comparison shows that four classifiers can be distinguished for the prediction quality: RF-PCT Random Forest of Predictive Clustering Trees, Kocev 2012, EBR Ensemble of Binary Relevance, Read et al., 2011, CLR Calibrated Label Ranking, Fürnkranz et al. 2008 and MLkNN Multi-label kNN, Zhang and Zhou 2007 with an advantage for the first two ensemble classifiers. Moreover, only RF-PCT competes with the fastest classifiers and is therefore considered as the most promising classifier for an interactive multi-label learning system.

Keywords : Interactive Machine Learning Multi-Label learning Comparative Analysis





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

Fuente: https://hal.archives-ouvertes.fr/



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