Volume 9 Number 9 (Sep. 2014)
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JSW 2014 Vol.9(9): 2276-2282 ISSN: 1796-217X
doi: 10.4304/jsw.9.9.2276-2282

An Attribute Reduction Algorithm Based on Rough Set Theory and an Improved Genetic Algorithm

Lian Chen, Hongling Liu, Zilong Wan
Computer Center, Nanchang University, Nanchang 330031, China

Abstract—Because the existing attribute reduction algorithms based on rough set theory and genetic algorithm have the main problems: the complexity in calculating fitness function and slow speed in convergence. An attribute reduction algorithm based on rough set theory and an improved genetic algorithm is proposed in this paper. In order to simplify the calculation of fitness function under the condition of keeping the algorithm correct, the relative importance of chromosome is used to define the fitness function. Beyond that, by introducing the core attributes into the initial population and using an improved mutation operator, the algorithm can not only maintain the feature of whole optimization, but also have a quicker convergence speed. The experimental results show that the algorithm can obtain the optimal attribute reduction set of information system quickly and efficiently.

Index Terms—rough set theory, relative attribute importance, genetic algorithm, relative importance of chromosome


Cite: Lian Chen, Hongling Liu, Zilong Wan, "An Attribute Reduction Algorithm Based on Rough Set Theory and an Improved Genetic Algorithm," Journal of Software vol. 9, no. 9, pp. 2276-2282, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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