Feature Extraction Using Genetic Algorithms

This paper summarizes our research on feature selection and extraction from high-dimensionality data sets using genetic algorithms. We have developed a GA-based approach utilizing a feedback linkage between feature evaluation and classification. That is, we carry out feature selection or feature extraction simultaneously with classifier design, through “genetic learning and evolution.” This approach combines a GA with a classifier system. The classifier can be a standard K-Nearest-Neighbor decision rule, a production rule or another classifier. Here we use a K-Nearest-Neighbor classifier as an example to introduce this general method. We apply this approach on a series of artificial test data and on real-world biological data to show the utility of this