Attribute Reductions of Formal Context Based on Information Entropy
CHEN Dongxiao1, LI Jinjin1,2, LIN Rongde1, CHEN Yingsheng1
1. Fujian Province University Key Laboratory of Computational Science, School of Mathematical Sciences, Huaqiao University, Quanzhou 362021 2. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000
Abstract：Attribute significances and attribute reduction are crucial in formal concept analysis. Some approaches to attribute reduction of formal context are proposed based on information entropy. Firstly, information entropy, conditional entropy and mutual information of formal context are defined, and attribute reduction by means of conditional entropy is conducted in consistent decision formal context. The equivalence between the granular consistency and the entropy consistency in decision formal context is produced. Secondly, limitary information entropy, limitary conditional entropy and limitary mutual information are proposed, and attribute reductions are conducted by means of limitary conditional entropy in inconsistent formal decision context. Finally, the attribute reduction algorithms of consistent and inconsistent formal decision contexts are proposed by the significance of attributes, and numerical experiments show the efficiency of the proposed algorithms.
陈东晓, 李进金, 林荣德, 陈应生. 基于信息熵的形式背景属性约简[J]. 模式识别与人工智能, 2020, 33(9): 786-798.
CHEN Dongxiao, LI Jinjin, LIN Rongde, CHEN Yingsheng. Attribute Reductions of Formal Context Based on Information Entropy. , 2020, 33(9): 786-798.
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2020, Vol. 33 
