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An Algorithm for Rule Extraction Based on Rough Set Theory

Changwei Wang

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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 condition 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 condition 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 condition attribute set without core-valued table would be attained by this algorithm.

Key concepts: Rough set, Decision table, Reduction (mathematics), Decision rule, Dominance-based rough set approach, Data mining, Algorithm, Set (abstract data type)

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