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2008•Journal of Computer ApplicationsRequires access

An improved algorithm for attribute reduction based on rough sets

Wang Xiao-ju

Open publisher page 2 citations

Abstract

At present, basically all attribute reduction based on Rough Sets is to extract the attribute core through the discernbility matrix, then calculate attribute reduction, but the method is still complex. This paper proposed and analyzed an attribute reduction algorithm of weighted mean attribute significance. The algorithm not only can get a reduction, but also does not need core calculating. It can reduce the computing effort and improve the computing efficiency. The algorithm is verified with the instance.

About this research paper

What this paper is about

At present, basically all attribute reduction based on Rough Sets is to extract the attribute core through the discernbility matrix, then calculate attribute reduction, but the method is still complex. This paper proposed and analyzed an attribute reduction algorithm of weighted mean attribute significance. The algorithm not only can get a reduction, but also does not need core calculating. It can reduce the computing effort and improve the computing efficiency. The algorithm is verified with the instance.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

At present, basically all attribute reduction based on Rough Sets is to extract the attribute core through the discernbility matrix, then calculate attribute reduction, but the method is still complex. This paper proposed and analyzed an attribute reduction algorithm of weighted mean attribute significance. The algorithm not only can get a reduction, but also does not need core calculating. It can reduce the computing effort and improve the computing efficiency. The algorithm is verified with the instance.

Key concepts: Reduction (mathematics), Rough set, Attribute domain, Computer science, Core (optical fiber), Algorithm, Granular computing, Data mining

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