Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete formation systems with fuzzy decisions

doi:10.3969/j.issn.1004-4132.2010.06.011

Journal of Systems Engineering and Electronics ›› 2010, Vol. 21 ›› Issue (6): 995-999.doi: 10.3969/j.issn.1004-4132.2010.06.011

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

Fang Yang1, Yanyong Guan1,*, Shujin Li2, and Lei Du1

1. School of Science, University of Jinan, Jinan 250022, P. R. China;
2. Department of Computer, Qilu Normal University, Jinan 250013, P. R. China

Online:2010-12-20 Published:2010-01-03

Abstract

Abstract:

A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description
are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.

Key words: rough sets, information systems, maximal tolerance class, attribute reduct, decision rules

Fang Yang, Yanyong Guan, Shujin Li, and Lei Du. Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions[J]. Journal of Systems Engineering and Electronics, 2010, 21(6): 995-999.

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Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

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