The Open Cybernetics & Systemics Journal

2015, 9 : 2774-2779
Published online 2015 October 30. DOI: 10.2174/1874110X01509012774
Publisher ID: TOCSJ-9-2774

An Improved Heuristic Attribute Reduction Algorithm Based on Information Entropy in Rough Set

Yang Su-Min , Meng Jie , Zhang zheng-Bao and Xie Zhi-Ying
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003,Henan, China and Department Of Information Engineering, Shijiazhuang Mechanical Engineering College, Shijiazhuang 050003, China.

ABSTRACT

At present, in rough set theory there are two kinds of heuristic attribute reduction algorithms, one is based on discernibility matrix, the other is based on mutual information. But if these algorithms are applied to the non-core information system, there will be much problems, such as too much calculation, excessive reduction, or insufficient reduction. So we propose an improved heuristic attribute reduction algorithm on the basis of rough set theory, in which the attribute importance is dependent on two factors, one is increment of mutual information, the other is information entropy. And we set the attribute with both the largest attribute importance and mutual information among all attributes as the core attribute, by which we solve the problem that causes the computational complexity increasing because of selecting the initial attribute randomly. By the proposed algorithm we can not only improve the efficiency of attribute reduction, but decrease the number of attribute reduction. The validity of the proposed algorithm is verified by two ways of the theoretic analysis and the simulation experiments.

Keywords:

Attribute reduction, mutual information, non-core information system, rough set.