文章基本信息

标题：Feature Selection Using Particle Swarm Optimization in Text Categorization
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作者：Mehdi Hosseinzadeh Aghdam ; Setareh Heidari
期刊名称：Journal of Artificial Intelligence and Soft Computing Research
电子版ISSN：2083-2567
出版年度：2015
卷号：5
期号：4
页码：231-238
DOI：10.1515/jaiscr-2015-0031
出版社：Walter de Gruyter GmbH
摘要：Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.