Multi-label classification algorithm derived from K-nearest neighbor rule with label dependencies

In multi-label learning, 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. Common approaches to multi-label classification learn independent classifiers for each category, and perform r...

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Bibliographic Details
Published in2008 16th European Signal Processing Conference pp. 1 - 5
Main Authors Younes, Zoulficar, Abdallah, Fahed, Denoeux, Thierry
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2008
Subjects
Europe
Learning systems
Measurement
Signal processing
Text categorization
Training
Vectors
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Summary:In multi-label learning, 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. Common approaches to multi-label classification learn independent classifiers for each category, and perform ranking or thresholding schemes in order to obtain multi-label classification. In this paper, we describe an original method for multi-label classification problems derived from a Bayesian version of the K-nearest neighbor (KNN), and taking into account the dependencies between labels. Experiments on benchmark datasets show the usefulness and the efficiency of the proposed method compared to other existing methods.
ISSN:2219-5491
2219-5491