Approximate Decision Rules and Matching Rules in Rough Set Based Classification Algorithms
粗糙集分类算法中的近似决策规则和规则匹配方法

In most cases,the decision rules inducted by rough set models are unacceptable as laws to classify new ob- jects. Approkimate decision rules and partial matching rules are proposed to overcome this problem. This paper dis- cusses two typical algorithms for the generation of approximate rules and comparatively analyzes their performance as proven by one case study. Furthermore, one more efficient algorithm is developed based on the two algorithms. This paper also describes the general measures used for matching rules,and a set of formulae are defined for complete matching and partial matching of decision rules according to dependency coefficient in rough set theory. The experi- ments show that the proposed approximation algorithm and measures for matching rules can further improve the matching possibility and correctness of basic decision rules generated based on rough set theory.