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Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection

School of Computer Science and Engineering, Chung-Ang University, 221, Heukseok-Dong, Dongjak-Gu, Seoul 06974, Korea
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Entropy 2019, 21(6), 602; https://doi.org/10.3390/e21060602
Received: 22 April 2019 / Revised: 11 June 2019 / Accepted: 17 June 2019 / Published: 18 June 2019
(This article belongs to the Special Issue Unconventional Methods for Particle Swarm Optimization)
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Abstract

Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods. View Full-Text
Keywords: multi-label text categorization; feature selection; hybrid search; evolutionary algorithm; particle swarm optimization multi-label text categorization; feature selection; hybrid search; evolutionary algorithm; particle swarm optimization
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Lee, J.; Park, J.; Kim, H.-C.; Kim, D.-W. Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection. Entropy 2019, 21, 602.

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