Rough Set Attributes Reduction Based on Adaptive PBIL Algorithm

摘要：

This paper presents a PBIL algorithm based on adaptive theory—giving that the traditional reduction of rough set is not unique and the process lasts for a long time. The learn probability and mutation rate of traditional PBIL algorithm can change adaptively by introducing the Systemic Entropy, then a self-learning and adaptive variability PBIL algorithm (APBIL) is formed. When it is applied to attributes reduction of rough set, it not only maintains the characteristics of global optimization but also reduces the correlation among attributes. Finally, the simplicity and effectiveness of the algorithm are demonstrated by an example.

作者: Lihua Wang Liangli Ma Qiang Bian Xiliang Zhao

作者单位: Department of Computer Engineering Naval University of Engineering, Wuhan, China College of Electrical and Information Engineering Naval University of Engineering Wuhan, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Information Theory and Information Security(2010 IEEE 国际信息论与信息安全会议)

会议地点: 北京

会议语种:英文

页码: 21-24

在线出版日期: 2010-12-17（万方平台首次上网日期，不代表论文的发表时间）