Department of Industrial Engineering and Management
Ming Hsin University of Science and Technology
1 Hsin-HsingRoad, Hsin-Fong, Hsinchu 304, R.O.C.
This paper proposes a method of genetic algorithm (GA) based neural network for feature selection that retains sufficient information for classification purposes. This method combines a genetic algorithm with an artificial neural network classifier, such as back-propagation (BP) neural classifier, radial basis function (RBF) classifier or learning vector quantization (LVQ) classifier. In this article, the genetic algorithm optimizes a feature vector by removing both irrelevant and redundant features and finds optimal ones. First, the procedure of the proposed algorithm is described and then the performance of this method is evaluated using two data sets. The results are compared with the genetic algorithm in combination with the k-nearest neighbor (KNN) classification rule. Our results suggest that GA based neural classifiers are robust and effective in finding optimal subsets of features from large data sets.
Keywords: genetic algorithm, back-propagation, radial basis function, learning vector quantization, k-nearest neighbor
(*Contact: E-mail Jeff@must.edu.tw )
Cite this article as: Te-Sheng Li, "Feature Selection for Classification by Using a Ga-Based Neural Network Approach," Journal of the Chinese Institute of Industrial Engineers, 23, 55-64 (2006).