Modified Cat Swarm Optimization Algorithm for Feature Selection of Support Vector Machines

被引：7

作者：

Lin, Kuan-Cheng ^{[1
]}

Huang, Yi-Hung ^{[2
]}

Hung, Jason C. ^{[3
]}

Lin, Yung-Tso ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Management Informat Syst, Taichung 40227, Taiwan

[2] Natl Taichung Univ Educ, Dept Math Educ, Taichung, Taiwan

[3] Overseas Chinese Univ, Dept Informat Management, Taichung, Taiwan

来源：

FRONTIER AND INNOVATION IN FUTURE COMPUTING AND COMMUNICATIONS | 2014年 / 301卷

关键词：

Swarm intelligence; Cat swarm optimization; Feature selection; Support vector machine; CLASSIFICATION;

D O I：

10.1007/978-94-017-8798-7_40

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection of support vector machines (SVMs). Experimental results demonstrate that the proposed MCSO algorithm provides better results in less time than basic CSO algorithms.

引用

页码：328 / 335

页数：8

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