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Fuzzy Modified Great Deluge Algorithm for Attribute Reduction

  • Majdi Mafarja
  • Salwani Abdullah
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 287)

Abstract

This paper proposes a local search meta-heuristic free of parameter tuning to solve the attribute reduction problem. Attribute reduction can be defined as the process of finding minimal subset of attributes from an original set with minimum loss of information. Rough set theory has been used for attribute reduction with much success. However, the reduction method inside rough set theory is applicable only to small datasets, since finding all possible reducts is a time consuming process. This motivates many researchers to find alternative approaches to solve the attribute reduction problem. The proposed method, Fuzzy Modified Great Deluge algorithm (Fuzzy-mGD), has one generic parameter which is controlled throughout the search process by using a fuzzy logic controller. Computational experiments confirmed that the Fuzzy-mGD algorithm produces good results, with greater efficiency for attribute reduction, when compared with other meta-heuristic approaches from the literature.

Keywords

Great Deluge Fuzzy Logic Attribute Reduction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of Information TechnologyBirzeit UniversityBirzeitPalestine
  2. 2.Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence TechnologyUniversiti KebangsaanBangiMalaysia

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