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Joint label-specific features and label correlation for multi-label learning with missing label

Joint label-specific features and label correlation for multi-label learning with missing label Existing multi-label learning classification algorithms ignore that class labels may be determined by some features in the original feature space. And only a partial label of each instance can be obtained for some real applications. Therefore, we propose a novel algorithm named joint Label-Specific features and Label Correlation for multi-label learning with Missing Label (LSLC-ML) and its optimized version to solve the above-mentioned problems. First, a missing label can be recovered by the learned positive and negative label correlations from the incomplete training data sets, then the label-specific features can be selected, finally the multi-label classification task can be modeled by combining the labelspecific feature selections, missing labels and positive and negative label correlations. The experimental results show that our algorithm LSLC-ML has strong competitiveness compared with some state-of-the-art algorithms in evaluation matrices when tested on benchmark multi-label data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Joint label-specific features and label correlation for multi-label learning with missing label

Applied Intelligence , Volume 50 (11) – Nov 8, 2020

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References (40)

Publisher
Springer Journals
Copyright
Copyright © Springer Science+Business Media, LLC, part of Springer Nature 2020
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-020-01715-2
Publisher site
See Article on Publisher Site

Abstract

Existing multi-label learning classification algorithms ignore that class labels may be determined by some features in the original feature space. And only a partial label of each instance can be obtained for some real applications. Therefore, we propose a novel algorithm named joint Label-Specific features and Label Correlation for multi-label learning with Missing Label (LSLC-ML) and its optimized version to solve the above-mentioned problems. First, a missing label can be recovered by the learned positive and negative label correlations from the incomplete training data sets, then the label-specific features can be selected, finally the multi-label classification task can be modeled by combining the labelspecific feature selections, missing labels and positive and negative label correlations. The experimental results show that our algorithm LSLC-ML has strong competitiveness compared with some state-of-the-art algorithms in evaluation matrices when tested on benchmark multi-label data sets.

Journal

Applied IntelligenceSpringer Journals

Published: Nov 8, 2020

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