10.4.1 Feature Selection in Machine Learning Using GA

In recent years there has been a significant increase in research on automatic image recognition in more realistic contexts involving noise, changing lighting conditions, and shifting viewpoints. The corresponding increase in difficulty in designing effective classification procedures for the important components of these more complex recognition problems has led to an interest in machine techniques as a possible strategy for automatically producing classification rules. This section describes part of a larger effort to apply machine learning techniques to such problems in an attempt to generate and improve the classification rules required for various recognition tasks.

The immediate problem attacked is that of texture recognition in the context of noise and changing lighting conditions. In this context standard rule induction systems like AQ15 produce sets of classification rules which are sub-optimal in two respects. First, there is a need to minimize the number of features actually used for classification, since each feature used adds to the design and manufacturing costs as well as the running time of a recognition system. At the same time there is a need to achieve high recognition rates in the presence of noise and changing environmental conditions. This section describes an approach 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...