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An Improved Algorithm for Attribute Reduction of Discernibility Matrix

Xiaomin Li

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Abstract

In this paper, based on the concept and property of knowledge dependence, an improved algorithm for attribute reduction of discernibility matrix is proposed. The decision table is processed according to the dependence of decision attribute to condition attribute. The discernibility matrix is simpler than traditional matrix, and the calculation complexity is lower. The minimum reduction can be obtained quickly, and the algorithm is proved high efficiency by actual examples.

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What this paper is about

In this paper, based on the concept and property of knowledge dependence, an improved algorithm for attribute reduction of discernibility matrix is proposed. The decision table is processed according to the dependence of decision attribute to condition attribute. The discernibility matrix is simpler than traditional matrix, and the calculation complexity is lower. The minimum reduction can be obtained quickly, and the algorithm is proved high efficiency by actual examples.

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Available abstract

In this paper, based on the concept and property of knowledge dependence, an improved algorithm for attribute reduction of discernibility matrix is proposed. The decision table is processed according to the dependence of decision attribute to condition attribute. The discernibility matrix is simpler than traditional matrix, and the calculation complexity is lower. The minimum reduction can be obtained quickly, and the algorithm is proved high efficiency by actual examples.

Key concepts: Decision table, Reduction (mathematics), Computer science, Rough set, Matrix (chemical analysis), Algorithm, Attribute domain, Property (philosophy)

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