2006Jisuanji gongcheng yu shejiRequires access

Research of algorithm for attribute value reduction based on rough set

Li Ming

Open publisher page 1 citations

Abstract

The nature and the process of attribute value reduction from the view of logic are analysed and based on this a discernibility matrix is constructed.Then this paper proposes a new algorithm for attribute value reduction based on rough set is proposed and the correct and feasibility of it are proved.The new algorithm is easy to be realized by programming.Not only it can get more concise decision rules,but also it doesn't break the information of inconsistent decision rules.The analysis of the realistic example shows that the algorithm is effective and feasible.

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What this paper is about

The nature and the process of attribute value reduction from the view of logic are analysed and based on this a discernibility matrix is constructed.Then this paper proposes a new algorithm for attribute value reduction based on rough set is proposed and the correct and feasibility of it are proved.The new algorithm is easy to be realized by programming.Not only it can get more concise decision rules,but also it doesn't break the information of inconsistent decision rules.The analysis of the realistic example shows that the algorithm is effective and feasible.

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Available abstract

The nature and the process of attribute value reduction from the view of logic are analysed and based on this a discernibility matrix is constructed.Then this paper proposes a new algorithm for attribute value reduction based on rough set is proposed and the correct and feasibility of it are proved.The new algorithm is easy to be realized by programming.Not only it can get more concise decision rules,but also it doesn't break the information of inconsistent decision rules.The analysis of the realistic example shows that the algorithm is effective and feasible.

Key concepts: Rough set, Computer science, Reduction (mathematics), Attribute domain, Algorithm, Value (mathematics), Set (abstract data type), Decision table

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