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Computational Intelligence and Neuroscience
Volume 2017, Article ID 6573623, 7 pages
https://doi.org/10.1155/2017/6573623
Research Article

A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm

Yuebin Su1,2 and Jin Guo1

1School of Information Science and Technology, Southwest Jiao Tong University, Chengdu 610031, China
2School of Mathematics and Statistics, Sichuan University of Science & Engineering, Zigong 643000, China

Correspondence should be addressed to Yuebin Su; moc.361@byshtam

Received 28 March 2017; Revised 25 June 2017; Accepted 5 July 2017; Published 15 August 2017

Academic Editor: Naveed Ejaz

Copyright © 2017 Yuebin Su and Jin Guo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

For data mining, reducing the unnecessary redundant attributes which was known as attribute reduction (AR), in particular, reducts with minimal cardinality, is an important preprocessing step. In the paper, by a coding method of combination subset of attributes set, a novel search strategy for minimal attribute reduction based on rough set theory (RST) and fish swarm algorithm (FSA) is proposed. The method identifies the core attributes by discernibility matrix firstly and all the subsets of noncore attribute sets with the same cardinality were encoded into integers as the individuals of FSA. Then, the evolutionary direction of the individual is limited to a certain extent by the coding method. The fitness function of an individual is defined based on the attribute dependency of RST, and FSA was used to find the optimal set of reducts. In each loop, if the maximum attribute dependency and the attribute dependency of condition attribute set are equal, then the algorithm terminates, otherwise adding a single attribute to the next loop. Some well-known datasets from UCI were selected to verify this method. The experimental results show that the proposed method searches the minimal attribute reduction set effectively and it has the excellent global search ability.