Rough Set Theory-Based Multi-Class Decision Attribute Reduction Algorithm and Its Application

摘要：

Rough Set Theory is an effective tool in dealing with vague and uncertainty information, attribute reduction is one of its important concept. Many attribute reduction algorithms have been proposed in recent years, but they are more suitable for two classes problem. For multi-class decision attributes problem, a new attribute reduction algorithm based on discernibility matrix is proposed in the paper, it makes great use of the advantage of decision attributes class information. In addition, we may draw an important conclusion that attribute reduction connects with class information in multi-class decision system, that is to say there will be deferent reduction results between deferent classes. The proposed algorithm can effectively reduce the computational complexity and increase reduction efficiency. Finally it is applied to diesel engine fault diagnosis, diagnosis result shows its feasibility and validity.

作者: Yitian Xu Laisheng Wang Yanping Sheng

作者单位: College of science China Agricultural University Beijing, 100083, China

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 722-725

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）