Feature selection for neural network classifiers using saliency and genetic algorithms

Abstract

In this paper the authors present the results of a research investigation on feature selection methods for neural network classifiers. As problems presented to computers for analysis become more complex and data dimensionality grows in size, traditional methods of feature extraction are being taxed beyond the limits of their usefulness. New methods of feature selection show promise in the laboratory, but need to be proven with real-world solutions. The purpose of this research is to compare the performance of newly proposed methods of selecting features on three challenging problems using non- artificial data. A feature saliency technique, and several variants of genetic algorithms, and random feature selection are compared and contrasted.