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2008•Journal of Hefei University of TechnologyRequires access

An improved algorithm for value reduction of attributes in decision tables based on rough set theory

Xuegang Hu

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Abstract

Attribute reduction and value reduction are two main contents in rough set theory.An improved value reduction algorithm suitable for decision tables,AVRIMC,is presented in this paper.For every row in the discernibility matrix,it utilizes the absorptivity and value core properties as optimization conditions to construct the latter items of this row.Experiment results show that this method is superior to the algorithm in related literature in time consumption.

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What this paper is about

Attribute reduction and value reduction are two main contents in rough set theory.An improved value reduction algorithm suitable for decision tables,AVRIMC,is presented in this paper.For every row in the discernibility matrix,it utilizes the absorptivity and value core properties as optimization conditions to construct the latter items of this row.Experiment results show that this method is superior to the algorithm in related literature in time consumption.

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Available abstract

Attribute reduction and value reduction are two main contents in rough set theory.An improved value reduction algorithm suitable for decision tables,AVRIMC,is presented in this paper.For every row in the discernibility matrix,it utilizes the absorptivity and value core properties as optimization conditions to construct the latter items of this row.Experiment results show that this method is superior to the algorithm in related literature in time consumption.

Key concepts: Rough set, Reduction (mathematics), Decision table, Algorithm, Value (mathematics), Construct (python library), Set (abstract data type), Matrix (chemical analysis)

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