关键词: 粗糙集, 知识约简, 二分mass函数, 熵, 属性重要性

Abstract: It is proved that solving the minimal reduction of decision table is a NP-hard problem. This paper puts on a heuristic algorithm based on rough set and evidence theory. It gives attribute information entropy by using the concept of equivalence partitioning of rough set, and defines the attribute importance to get the core of the knowledge. It establishes an evidence function for each attribute by the concept of dichotomous mass functions, combining which to get the evidence importance of each attribute. Set the core as the start of the algorithm and make size of attributes importance as heuristic information until it meets the reduction condition. Examples show that it can find the core and reduction quickly, and the reduction used in classification accuracy is higher.

Key words: rough set, reduction of knowledge, dichotomous mass functions, entropy, importance of attributes