基于模糊格的超盒粒计算分类器

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Abstract

Representation, relation and operation of granules are the main research content of granular computing. A hyperbox granule is represented by a vector including a beginning point and an end point. The inconsistency between the partial ordering relation in vector space and the partial ordering relation in hyperbox granule space is analyzed and then eliminated by the order-preserving function. The fuzzy inclusion relation between two hyperbox granules is formed by nonlinear positive valuation function and the order-preserving function between the lattice and its dual lattice. The join operator and decomposition operator between two granules are designed to achieve the granules with different granularity. The algebraic system which is composed of hyperbox granule set, fuzzy inclusion relation and the operators between two granules is proved as fuzzy lattice. Hyperbox granular computing classifiers are formed based on fuzzy lattice, and verified by classification problems on machine learning dataset. The experimental results show that hyperbox granular computing classifiers have a generalization ability comparable to that of fuzzy lattice reasoning classifiers with less number of hyperbox granules.

Key words： Fuzzy Lattice Granular Computing Hyperbox Granule Fuzzy Inclusion Relation

Received: 26 June 2012

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	Articles by authors
	LIU Hong-Bing
	WU Chang-An
	XIONG Sheng-Wu

Cite this article:

LIU Hong-Bing,WU Chang-An,XIONG Sheng-Wu. Hyperbox Granular Computing Classifiers Based on Fuzzy Lattices[J]. , 2013, 26(8): 777-786.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2013/V26/I8/777

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