2011Unpublished venueRequires access

Study of algorithm for rule extraction based on rough set theory

XianWen Luo

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Abstract

The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification. In this paper, an algorithm on value reduction and for extracting decision rule based on the membership function is proposed. All the decision rules on decision table and the minimal rule set of reduced conditional attribute set without core-valued table would be attained by this algorithm.

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What this paper is about

The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification. In this paper, an algorithm on value reduction and for extracting decision rule based on the membership function is proposed. All the decision rules on decision table and the minimal rule set of reduced conditional attribute set without core-valued table would be attained by this algorithm.

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Available abstract

The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification. In this paper, an algorithm on value reduction and for extracting decision rule based on the membership function is proposed. All the decision rules on decision table and the minimal rule set of reduced conditional attribute set without core-valued table would be attained by this algorithm.

Key concepts: Rough set, Decision table, Dominance-based rough set approach, Decision rule, Reduction (mathematics), Data mining, Computer science, Algorithm

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