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Attribute Reduction in Decision-Theoretic Rough Set Models Using Genetic Algorithm

  • Srilatha Chebrolu
  • Sriram G. Sanjeevi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

Real world data may contain inconsistencies, uncertainty and noise. Rough set model is a mathematical methodology in data analysis to deal with inconsistent and imperfect knowledge. Various probabilistic approaches to rough set model are proposed. Decision-theoretic rough set model (DTRSM) is one of the probabilistic approaches to rough set model. This paper proposes an attribute reduction algorithm in DTRSM, through region preservation. Attribute reduction is the process of identifying and removing redundant and irrelevant attributes from huge data sets, reducing its volume. The reduced data set can be much more effectively analyzed. Attribute reduction in DTRSM through region preservation is an optimization problem, thus Genetic Algorithm (GA) is used to achieve this optimization. Experiment results on discrete data sets are compared with local optimization approach based on discernibility matrix method and has been shown that GA can be effectively and efficiently used to achieve global minimal reduct.

Keywords

Attribute Reduction Decision-Theoretic Rough Set Model Genetic Algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Srilatha Chebrolu
    • 1
  • Sriram G. Sanjeevi
    • 1
  1. 1.Department of Computer Science and EngineeringNIT WarangalIndia

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