A PSO-based multi-objective multi-label feature selection method in classification

Feature selection is an important data preprocessing technique in multi-label classification. Although a large number of studies have been proposed to tackle feature selection problem, there are a few cases for multi-label data. This paper studies a multi-label feature selection algorithm using an i...

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Bibliographic Details
Published in:Scientific reports 2017-03, Vol.7 (1), p.376-376, Article 376
Main Authors: Zhang, Yong, Gong, Dun-wei, Sun, Xiao-yan, Guo, Yi-nan
Format: Article
Language:English
Subjects:
631/114/1386
639/705/258
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
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Summary:Feature selection is an important data preprocessing technique in multi-label classification. Although a large number of studies have been proposed to tackle feature selection problem, there are a few cases for multi-label data. This paper studies a multi-label feature selection algorithm using an improved multi-objective particle swarm optimization (PSO), with the purpose of searching for a Pareto set of non-dominated solutions (feature subsets). Two new operators are employed to improve the performance of the proposed PSO-based algorithm. One operator is adaptive uniform mutation with action range varying over time, which is used to extend the exploration capability of the swarm; another is a local learning strategy, which is designed to exploit the areas with sparse solutions in the search space. Moreover, the idea of the archive, and the crowding distance are applied to PSO for finding the Pareto set. Finally, experiments verify that the proposed algorithm is a useful approach of feature selection for multi-label classification problem.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-00416-0