Sélection d'une méthode de classification multi-label pour un système interactif
Résumé
The objective of this paper is to evaluate the ability of 12 multi-label classification algorithms at learning, in a short time, with few training examples. Experimental results highlight significant differences for 3 selected evaluation measures: Log-Loss, Ranking-Loss, Learning/Prediction time, and the best results are obtained with: Multi-label k Nearest neighbors (ML-kNN ), followed by Ensemble of Classifier Chains (ECC) and Ensemble of Binary Relevance (EBR).
Domaines
Apprentissage [cs.LG]
Origine : Fichiers produits par l'(les) auteur(s)
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