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Decision Table Reduction Algorithm Based on Knowledge Quantity

Liang Chun-hua

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Abstract

Attribute reduction is an important problem in rough set theory.This paper presents the concept of the knowledge quantity of decision attributes with relation to given condition attributes and redefines the importance of the attribute in decision table.Then,a new reduction algorithm based on knowledge quantity is developed.A test shows that this algorithm is effective.

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What this paper is about

Attribute reduction is an important problem in rough set theory.This paper presents the concept of the knowledge quantity of decision attributes with relation to given condition attributes and redefines the importance of the attribute in decision table.Then,a new reduction algorithm based on knowledge quantity is developed.A test shows that this algorithm is effective.

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Available abstract

Attribute reduction is an important problem in rough set theory.This paper presents the concept of the knowledge quantity of decision attributes with relation to given condition attributes and redefines the importance of the attribute in decision table.Then,a new reduction algorithm based on knowledge quantity is developed.A test shows that this algorithm is effective.

Key concepts: Decision table, Rough set, Reduction (mathematics), Relation (database), Table (database), Computer science, Dominance-based rough set approach, Algorithm

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