期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：2
页码：2095-2098
出版社：TechScience Publications
摘要：Feature selection is an optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. Feature selection is of great importance in pattern classification, medical data processing, machine learning, and data mining applications. In this paper, continuous particle swarm optimization (PSO) is used to implement a feature selection in wrapper based method, and the k-nearest neighbor classification serve as a fitness function of PSO for the classification problem. The PSO based feature selection method is applied to the features extracted from the Lung CT scan images. Experimental results show that modified PSO feature selection method simplifies features effectively and obtains a higher classification accuracy compared to the basic PSO Feature selection method
关键词：Feature Selection; PSO; Population; Fitness function
