A Simple Fitness Function for Minimum Attribute Reduction. (3rd August 2015)

• Record Type: Journal Article
• Authors: Su, Yuebin; Guo, Jin; Li, Zejun
• Other Names: Abiyev Rahib H. Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2015(2015)
• Publication Date: 2015-08-03
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2015/921487
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The goal of minimal attribute reduction is to find the minimal subsetR of the condition attribute setC such thatR has the same classification quality asC . 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.