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Feature selection based on an improved cat swarm optimization algorithm for big data classification

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Abstract

Feature selection, which is a type of optimization problem, is generally achieved by combining an optimization algorithm with a classifier. Genetic algorithms and particle swarm optimization (PSO) are two commonly used optimal algorithms. Recently, cat swarm optimization (CSO) has been proposed and demonstrated to outperform PSO. However, CSO is limited by long computation times. In this paper, we modify CSO to present an improved algorithm, ICSO. We then apply the ICSO algorithm to select features in a text classification experiment for big data. Results show that the proposed ICSO outperforms traditional CSO. For big data classification, the results show that using term frequency-inverse document frequency (TF-IDF) with ICSO for feature selection is more accurate than using TF-IDF alone.

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Authors and Affiliations

  1. Department of Management Information Systems, National Chung Hsing University, Taichung, 40227, Taiwan, ROC

    Kuan-Cheng Lin & Kai-Yuan Zhang

  2. Department of Mathematics Education, National Taichung University of Education, Taichung, Taiwan, ROC

    Yi-Hung Huang

  3. Department of Information Technology, Overseas Chinese University, Taichung, Taiwan, ROC

    Jason C. Hung

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan

    Neil Yen

Authors
  1. Kuan-Cheng Lin
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  2. Kai-Yuan Zhang
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  3. Yi-Hung Huang
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  4. Jason C. Hung
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  5. Neil Yen
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Correspondence to Jason C. Hung.

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Lin, KC., Zhang, KY., Huang, YH. et al. Feature selection based on an improved cat swarm optimization algorithm for big data classification. J Supercomput 72, 3210–3221 (2016). https://doi.org/10.1007/s11227-016-1631-0

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  • DOI: https://doi.org/10.1007/s11227-016-1631-0

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