基于最大粒的规则获取算法

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Abstract Granular computing (GrC) is a method for simulating human thinking and solving complicated problems. It is a powerful tool for solving complicated problems, mining massive data sets, and dealing with fuzzy information. In this paper, the shortcoming of the traditional rule extraction methods is presented, and then the granularity principle of rule extraction is analyzed based on granular computing method. The essence of attribute reduction is to choose a maximum approximation partition space of decision-making knowledge space, and the rules acquired from maximum approximation partition space may not be the simplest. Therefore, a rule extraction algorithm based on granular computing is proposed. In the proposed algorithm, the rules based on maximal granule can be acquired from information system in a hierarchical knowledge space in top-down manner, and the results of the simulation experiments illustrate that the generalization ability of rough set method is improved.

Key words： Attribution Reduction Knowledge Acquisition Granular Computing Knowledge Space Quotient Space

Received: 08 December 2010

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	ZHANG Qing-Hua
	WANG Guo-Yin
	LIU Xian-Quan

Cite this article:

ZHANG Qing-Hua,WANG Guo-Yin,LIU Xian-Quan. Rule Acquisition Algorithm Based on Maximal Granule[J]. , 2012, 25(3): 388-396.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I3/388

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