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

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 i...

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Bibliographic Details
Published inEURASIP journal on advances in signal processing Vol. 2011
Main Authors Younes, Zoulficar, Abdallah, Fahed, Denoeux, Thierry, Snoussi, Hichem
Format Journal Article
LanguageEnglish
Published SpringerOpen 2011
Subjects
Artificial Intelligence
Computer Science
k-NN
dependency
Bayes' rule
Multi-label
classification
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Summary: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.
ISSN:1687-6172
1687-6180
DOI:10.1155/2011/645964