Attribute selection method based on a hybrid BPNN and PSO algorithms

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• 作者：Cong Jin^a ; ^{jincong@mail.ccnu.edu.cn} ; Shu-Wei Jin^b ; Li-Na Qin^a
• 关键词：Reduction dimensionality ; Attribute selection ; Back Propagation Neural Network ; Particle Swarm Optimization ; Input output correlation
• 刊名：Applied Soft Computing Journal
• 出版年：2012
• 期刊代码：9_15684946
• 类别：cp
• 出版时间：August, 2012
• 卷：12
• 期：8
• 页码：2147-2155
• 文件大小：456 K

摘要

High dimensional data contain many redundant or irrelevant attributes, which will be difficult for data mining and a variety of pattern recognition. When implementing data mining or a variety of pattern recognition on high dimensional space, it is necessary to reduce the dimension of high dimensional space. In this paper, a new attribute importance measure and selection methods based on attribute ranking was proposed. In proposed attribute selection method, input output correlation (IOC) is applied for calculating attribute鈥?importance, and then sorts them according to descending order. The hybrid of Back Propagation Neural Network (BPNN) and Particle Swarm Optimization (PSO) algorithms is also proposed. PSO is used to optimize weights and thresholds of BPNN for overcoming the inherent shortcoming of BPNN. The experiment results show the proposed attribute selection method is an effective preproceesing technology.