Effective Feature Selection with Particle Swarm Optimization based One-dimension Searching

摘要：

Forming an efficient feature space for classification problems is a grand challenge in pattern recognition. Many optimization algorithms are adopted to do feature selection, but these algorithms do searching in multi-dimensions space and always cannot get the optimal feature subset. In this paper, a feature selection method with Particle Swarm Optimization based one-dimension searching is proposed to improve the classification performance. Experimental results show that the proposed method can do feature selection more effectively than the compared method and get much higher classification accuracy.

作者: Jun Wang Yan Zhao Ping Liu

作者单位: Department of Electronics Engineering,Shantou University,No.243 Daxue Road,Shantou,Guangdong,515063,Peoples Republic of China

会议类型: 国际会议

会议名称: The 3rd International Symposium on Systems and Control in Aeronautics and Astronautics(第三届航空航天系统与控制国际会议 ISSCAA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 679-682

在线出版日期: 2010-01-08（万方平台首次上网日期，不代表论文的发表时间）