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2013•Computer Engineering and Applications JournalOpen access

Rough set reduction method of attribute based on importance of attribute

Liao Qimin

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Abstract

Attribute reduction in information system is an important step during knowledge acquisition using Rough set. This paper focuses on the research of feature selection, deleting superfluous attributes in an information system. The new algorithm begins with the attribute significance, adopting iterative feature selection standard, making the selected feature attribute set get smaller, thus it acquires the reduction of information system. The experiment demonstrates that this method is feasible and effective.

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What this paper is about

Attribute reduction in information system is an important step during knowledge acquisition using Rough set. This paper focuses on the research of feature selection, deleting superfluous attributes in an information system. The new algorithm begins with the attribute significance, adopting iterative feature selection standard, making the selected feature attribute set get smaller, thus it acquires the reduction of information system. The experiment demonstrates that this method is feasible and effective.

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Available abstract

Attribute reduction in information system is an important step during knowledge acquisition using Rough set. This paper focuses on the research of feature selection, deleting superfluous attributes in an information system. The new algorithm begins with the attribute significance, adopting iterative feature selection standard, making the selected feature attribute set get smaller, thus it acquires the reduction of information system. The experiment demonstrates that this method is feasible and effective.

Key concepts: Rough set, Attribute domain, Reduction (mathematics), Data mining, Feature selection, Computer science, Set (abstract data type), Feature (linguistics)

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