A NOVEL KNOWLEDGE REDUCTION METHOD BASED ON RANK CORRELATION ANALYSIS

摘要：

Dominance-based rough set approach has recently become a routine method to deal with preference-ordered data, and knowledge reduction method based on rough set theory has been proposed. However, the results obtained are usually short of statistical significance. In this paper, non- parametric methods in statistics are introduced to analyze ordered information systems and ordered decision tables. Spearman and Kendall rank correlation coefficient are respectively used as new measures of attribute sets correlation. Based on these measures, a new method of knowledge reduction of the ordered information systems and the ordered decision tables using nonparametric rank statistics is presented. It can be proved that there are some relationships between the rough set theory and the nonparametric statistical methods. The numerical experiments show that the approach proposed is feasible, and it can provide a statistical evidence for rough set method.

关键词： Rough set dominance-based rough set approach ordinal data rank correlation knowledge reduction

作者: JIANG BAI LI-LI WEI

作者单位: School of Mathematics & Computer Science, Ningxia University, Yinchuan 750021, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 153-159

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）