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- W310042923 abstract "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)." @default.
- W310042923 created "2016-06-24" @default.
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- W310042923 date "2014-01-28" @default.
- W310042923 modified "2023-09-28" @default.
- W310042923 title "Sélection d'une méthode de classification multi-label pour un système interactif" @default.
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- W310042923 hasPublicationYear "2014" @default.
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