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

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• 作者：Kuan-Cheng Lin ; Kai-Yuan Zhang ; Yi-Hung Huang…
• 刊名：The Journal of Supercomputing
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：72
• 期：8
• 页码：3210-3221
• 全文大小：525 KB
• 刊物类别：Computer Science
• 刊物主题：Programming Languages, Compilers and Interpreters
  Processor Architectures
  Computer Science, general
  
• 出版者：Springer Netherlands
• ISSN：1573-0484
• 卷排序：72

文摘

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.KeywordsCat swarm optimizationFeature selectionSupport vector machineBig data classification