文章基本信息

标题：A Modified Group Search Optimizer for Feature Selection and Parameter Determination of SVM
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作者：K. Joshil Raj ; S Siva Sathya ; Kalyan Nandi 等
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2015
卷号：5
期号：2
页码：32-36
出版社：International Journal of Soft Computing & Engineering
摘要：Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Group Search Optimizer (GSO) is a new population based optimization algorithm inspired by animal searching behavior for developing optimum searching strategies to find out solutions for continuous optimization problems. This paper presents an experimental analysis of modifications to classical GSO & studies its effects on a GSO-SVM hybrid combination for feature selection and kernel parameters optimization. In the proposed algorithm, three modifications are introduced over classical GSO to improve its global search mechanism. The quality and effectiveness of the proposed methodology has been evaluated on standard machine learning datasets.
关键词：Evolutionary algorithm; Group Search Optimizer;GSO; Support Vector Machine; Machine learning; Feature;Selection; Kernel parameters