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Knowledge reduction in decision-theoretic rough set model based on connection degree

Ping Lv, Jin Gui Qian, Yuntao Qian

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Abstract

Knowledge reduction is one of the most important research issues in decision-theoretic rough set model. This paper first defines a new attribute measure for a reduct preserving boundary region partition, then constructs a connection degree to evaluate the different candidate reducts, and finally proposes a knowledge reduction algorithm for decision-theoretic rough set model. Example analysis shows that this algorithm is valid.

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What this paper is about

Knowledge reduction is one of the most important research issues in decision-theoretic rough set model. This paper first defines a new attribute measure for a reduct preserving boundary region partition, then constructs a connection degree to evaluate the different candidate reducts, and finally proposes a knowledge reduction algorithm for decision-theoretic rough set model. Example analysis shows that this algorithm is valid.

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Available abstract

Knowledge reduction is one of the most important research issues in decision-theoretic rough set model. This paper first defines a new attribute measure for a reduct preserving boundary region partition, then constructs a connection degree to evaluate the different candidate reducts, and finally proposes a knowledge reduction algorithm for decision-theoretic rough set model. Example analysis shows that this algorithm is valid.

Key concepts: Rough set, Reduct, Dominance-based rough set approach, Decision table, Reduction (mathematics), Partition (number theory), Computer science, Degree (music)

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