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2008•Journal of Henan Normal UniversityRequires access

Heuristic Algorithm for Reduction Based on Attribute Significance

Xuegang Hu

Open publisher page 1 citations

Abstract

Attribute reduction is one of the key problems in the research on rough set theory.In order to avoid variety bias,a new messure of attribute significance is defined.And based on this method,a new algorithm of attribute reduction is proposed.With the core attributes as the initial reduction,this algorithm uses the attribute significance as heuristic information,and finds the minimal reduction.The results from an example show that this algorithm is effective.

About this research paper

What this paper is about

Attribute reduction is one of the key problems in the research on rough set theory.In order to avoid variety bias,a new messure of attribute significance is defined.And based on this method,a new algorithm of attribute reduction is proposed.With the core attributes as the initial reduction,this algorithm uses the attribute significance as heuristic information,and finds the minimal reduction.The results from an example show that this algorithm is effective.

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Available abstract

Attribute reduction is one of the key problems in the research on rough set theory.In order to avoid variety bias,a new messure of attribute significance is defined.And based on this method,a new algorithm of attribute reduction is proposed.With the core attributes as the initial reduction,this algorithm uses the attribute significance as heuristic information,and finds the minimal reduction.The results from an example show that this algorithm is effective.

Key concepts: Rough set, Reduction (mathematics), Attribute domain, Heuristic, Algorithm, Set (abstract data type), Variety (cybernetics), Core (optical fiber)

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