摘要
区间值决策信息系统是单值信息系统的一种推广,借助于属性区间值的相似程度在区间值决策系统上引入α极大相容类的概念,定义了一种新的条件信息熵,提出了相对属性内(外)重要度的度量方法,进一步,给出基于α条件信息熵的启发式相对约简算法,通过实验验证了该算法的有效性。
Interval-valued decision information systems are generalized models of single-valued information sys- tems. A kind of a maximal tolerance class is introduced by similarity grade of attribute' s interval-value in inter- val-valued decision system. This paper defines new conditional entropy among attributes in interval-valued informa- tion systems and proposes two types of measurement of relative attribute importance, which is inner attribute impor- tance and outer attribute importance. Furthermore, a heuristic relative attribute reduction algorithm based on a condi- tional information entropy is given, and the validity of the algorithm is illustrated by some experiments.
出处
《计算机工程与应用》
CSCD
2012年第27期114-118,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.71031006
No.70971080
No.60903110)
关键词
区间值决策系统
α极大相容类
α近似约简
相对属性重要度
interval-valued decision system
a maximal consistent class
a approximation reduction
relative attri- bute importance