A filter-based bare-bone particle swarm optimization algorithm for unsupervised feature selection

Due to good exploration capability, particle swarm optimization (PSO) has shown advantages on solving supervised feature selection problems. Compared with supervised and semi-supervised cases, unsupervised feature selection becomes very difficult as a result of no label information. This paper studi...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2019-08, Vol.49 (8), p.2889-2898
Main Authors: Zhang, Yong, Li, Hai-Gang, Wang, Qing, Peng, Chao
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Feature selection
Machines
Manufacturing
Mechanical Engineering
Optimization algorithms
Particle swarm optimization
Processes
Production planning
Redundancy
Strategy
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Summary:Due to good exploration capability, particle swarm optimization (PSO) has shown advantages on solving supervised feature selection problems. Compared with supervised and semi-supervised cases, unsupervised feature selection becomes very difficult as a result of no label information. This paper studies a novel PSO-based unsupervised feature selection method, called filter-based bare-bone particle swarm optimization algorithm (FBPSO). Two filter-based strategies are proposed to speed up the convergence of the algorithm. One is a space reduction strategy based on average mutual information, which is used to remove irrelevant and weakly relevant features fast; another is a local filter search strategy based on feature redundancy, which is used to improve the exploitation capability of the swarm. And, a feature similarity-based evaluation function and a parameter-free update strategy of particle are introduced to enhance the performance of FBPSO. Experimental results on some typical datasets confirm superiority and effectiveness of the proposed FBPSO.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-019-01420-9