Genetic algorithms as a tool for feature selection in machine learning

Abstract

An approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real-world data is described. The approach involves the use of genetic algorithms as a front end to a traditional rule induction system in order to identify and select the best subset of features to be used by the rule induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate that there are significant advantages to the approach in this domain.<>