Feature Extraction Using Genetic Algorithms

Abstract

We propose a method of feature extraction to improve the performance of pattern recognition technique. The extracted features are defined as polynomial expressions which are composed of the original input information. Polynomial expressions are searched by genetic algorithms. In order to evaluate the effectiveness of the proposed method, we apply the k nearest neighbor classifier to the classification rule. Experiments were performed for the artificial data and the acoustic diagnosis for compressors as the real world task. The results show that the feature extraction with genetic algorithm is effective for these data.