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Computational Intelligence and Neuroscience
Volume 2015, Article ID 921487, 6 pages
http://dx.doi.org/10.1155/2015/921487
Research Article

A Simple Fitness Function for Minimum Attribute Reduction

1School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China
2School of Science, Sichuan University of Science and Engineering, Zigong 643000, China

Received 18 August 2014; Revised 29 November 2014; Accepted 18 December 2014

Academic Editor: Rahib H. Abiyev

Copyright © 2015 Yuebin Su et al. 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

The goal of minimal attribute reduction is to find the minimal subset of the condition attribute set such that has the same classification quality as . This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.