摘要： 针对区间值信息系统中的属性约简问题，引入α-极大相容类的概念，定义区间值信息系统的属性间依赖度和信息熵，提出相应属性内(外)重要度的度量方法，给出一种统一的启发式属性约简算法，通过实验验证该算法的有效性，并分析不同相似水平α对约简结果的影响。

关键词: 区间值信息系统, 近似约简, α-极大相容类, 属性重要度, 信息熵, 属性依赖度

Abstract: Interval-valued attribute reduction is the core problem in interval-valued information systems research. By introducing a kind of α maximal tolerance class, this paper defines the attribute dependency and conditional entropy among attributes in interval-valued information systems, and proposes two types of attribute importance measurement, which has inner attribute importance and outer attribute importance. A uniform heuristic attribute reduction algorithm is given. The validity of the algorithm is illustrated by some experiments and the effect of different level of similarity α for reduction is investigated as well.

Key words: interval-valued information system, approximation reduction, α-maximal consistent class, attribute importance, information entropy, attribute dependency

中图分类号: 

• TP391