A Rough Set-Based Method for Updating Decision Rules on Attribute Values%26apos; Coarsening and Refining

作者：Chen Hongmei; Li Tianrui^*; Luo Chuan; Horng Shi Jinn; Wang Guoyin

来源：IEEE Transactions on Knowledge and Data Engineering, 2014, 26(12): 2886-2899.

DOI：10.1109/TKDE.2014.2320740

• Inconsistent decision system
• rough set theory
• incremental learning
• decision rule
• attribute reduction
• approximations

Rule induction method based on rough set theory (RST) has received much attention recently since it may generate a minimal set of rules from the decision system for real-life applications by using of attribute reduction and approximations. The decision system may vary with time, e.g., the variation of objects, attributes and attribute values. The reduction and approximations of the decision system may alter on Attribute Values%26apos; Coarsening and Refining (AVCR), a kind of variation of attribute values, which results in the alteration of decision rules simultaneously. This paper aims for dynamic maintenance of decision rules w:r:t: AVCR. The definition of minimal discernibility attribute set is proposed firstly, which aims to improve the efficiency of attribute reduction in RST. Then, principles of updating decision rules in case of AVCR are discussed. Furthermore, the rough set-based methods for updating decision rules in the inconsistent decision system are proposed. The complexity analysis and extensive experiments on UCI data sets have verified the effectiveness and efficiency of the proposed methods.

• 出版日期2014-12
• 单位重庆大学; 西南交通大学