4.5 Article

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

期刊

JOURNAL OF SUPERCOMPUTING
卷 72, 期 8, 页码 3210-3221

出版社

SPRINGER
DOI: 10.1007/s11227-016-1631-0

关键词

Cat swarm optimization; Feature selection; Support vector machine; Big data classification

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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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