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The Journal of Supercomputing

, Volume 72, Issue 8, pp 3210–3221 | Cite as

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

  • Kuan-Cheng Lin
  • Kai-Yuan Zhang
  • Yi-Hung Huang
  • Jason C. HungEmail author
  • Neil Yen
Article

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.

Keywords

Cat swarm optimization Feature selection Support vector machine Big data classification 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Management Information SystemsNational Chung Hsing UniversityTaichungTaiwan, ROC
  2. 2.Department of Mathematics EducationNational Taichung University of EducationTaichungTaiwan, ROC
  3. 3.Department of Information TechnologyOverseas Chinese UniversityTaichungTaiwan, ROC
  4. 4.School of Computer Science and EngineeringThe University of AizuAizu-WakamatsuJapan

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