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Article Dans Une Revue EURASIP Journal on Advances in Signal Processing Année : 2011

A dependent multi-label classification method derived from the k-nearest neighbor rule

Résumé

In multi-label classification, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly-used approach for multi-label classification is where a binary classifier is learned independently for each possible class. However, multi-labeled data generally exhibit relationships between labels, and this approach fails to take such relationships into account. In this paper, we describe an original method for multi-label classification problems derived from a Bayesian version of the k-Nearest Neighbor (k-NN) rule. The method developed here is an improvement on an existing method for multi-label classification, namely multi-label k-NN, which takes into account the dependencies between labels. Experiments on simulated and benchmark datasets show the usefulness and the efficiency of the proposed approach as compared to other existing methods.
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Dates et versions

hal-00655629 , version 1 (31-12-2011)

Identifiants

Citer

Zoulficar Younes, Fahed Abdallah, Thierry Denoeux, Hichem Snoussi. A dependent multi-label classification method derived from the k-nearest neighbor rule. EURASIP Journal on Advances in Signal Processing, 2011, 2011, pp.Article ID 645964. ⟨10.1155/2011/645964⟩. ⟨hal-00655629⟩
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