摘要
提出一种基于粗糙集和证据理论的两阶段决策规则提取算法,该算法首先利用粗糙集中属性缩减的思想,找出每条规则中的重要条件属性集合,然后再基于证据理论中证据结合的思想进一步去掉重要条件属性集中的冗余条件属性,从而得到最终的决策规则.所给算法简化了属性集的约简,对高维数据也是可行的.实验结果表明,利用该算法能够挖掘出高质量的决策规则.
This paper presents a two-phase algorithm for extraction of decision rules based on rough set and evidence theory. In the algorithm the thinking of reducing feature of rough set theory was used to get the important feature sets of each rule. Then on the basis of the thinking of evidence combination of evidence the redundant features of the important feature sets was cut so as to get decision rules. The two-phase algorithm presented in this paper simplifies the reducing of feature sets. And it is feasible for high dimensional data. The result of experiment shows that it can get fine decision rules.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2007年第4期577-581,共5页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:6043302060673099)
吉林大学985工程项目基金
关键词
粗糙集理论
证据理论
决策规则提取
rough set
evidence theory
extraction of decision rule
