A Simple Fitness Function for Minimum Attribute Reduction.
The goal of minimal attribute reduction is to find the minimal subset R of the condition attribute set C such that R has the same classification quality as C. 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.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China ; School of Science, Sichuan University of Science and Engineering, Zigong 643000, China.
Yuebin
Y
Su
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Jin
J
Guo
School of Science, Sichuan University of Science and Engineering, Zigong 643000, China.
Zejun
Z
Li
D000465:Algorithms; D017711:Nonlinear Dynamics; D016008:Statistical Distributions; D013269:Stochastic Processes