Improving a Rule Induction System Using Genetic Algorithms
H. Vafaie · 1994
The field of automatic image recognition presents a variety of difficult classification problems involving the identification of important scene components in the presence of noise, changing lighting conditions. and shifting viewpoints. This chapter describes part of a larger effort to apply machine learning techniques to such problems in an attempt to automatically generate and improve the classification rules required for various recognition tasks. The immediate problem attacked is that of texture recognition in the presence of noise and changing lighting conditions. In this context., standard rule induction systems like AQl5 produce sets of classification rules that are not necessarily optimal with respect to (1) the need to minimize the number of features actually used for classification and (2) the need to achieve high recognition rates with noisy data This chapter describes one of several multistrategy approaches being explored to improve the usefulness of machine learning techniques for such problems. The approach described here involves the use of genetic algorithms as a "front end " to traditional rule induction systems in order to identify and select the best subset of features to be used by the rule induction