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Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification

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posted on 24.03.2021, 01:51 by B Tran, Bing XueBing Xue, Mengjie ZhangMengjie Zhang
© 1997-2012 IEEE. With a global search mechanism, particle swarm optimization (PSO) has shown promise in feature selection (FS). However, most of the current PSO-based FS methods use a fix-length representation, which is inflexible and limits the performance of PSO for FS. When applying these methods to high-dimensional data, it not only consumes a significant amount of memory but also requires a high computational cost. Overcoming this limitation enables PSO to work on data with much higher dimensionality which has become more and more popular with the advance of data collection technologies. In this paper, we propose the first variable-length PSO representation for FS, enabling particles to have different and shorter lengths, which defines smaller search space and therefore, improves the performance of PSO. By rearranging features in a descending order of their relevance, we facilitate particles with shorter lengths to achieve better classification performance. Furthermore, using the proposed length changing mechanism, PSO can jump out of local optima, further narrow the search space and focus its search on smaller and more fruitful area. These strategies enable PSO to reach better solutions in a shorter time. Results on ten high-dimensional datasets with varying difficulties show that the proposed variable-length PSO can achieve much smaller feature subsets with significantly higher classification performance in much shorter time than the fixed-length PSO methods. The proposed method also outperformed the compared non-PSO FS methods in most cases. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Preferred citation

Tran, B., Xue, B. & Zhang, M. (2019). Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification. IEEE Transactions on Evolutionary Computation, 23(3), 473-487. https://doi.org/10.1109/TEVC.2018.2869405

Journal title

IEEE Transactions on Evolutionary Computation

Volume

23

Issue

3

Publication date

01/06/2019

Pagination

473-487

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

ISSN

1089-778X

eISSN

1941-0026