This paper describes an approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real world data. The approach involves the use of genetic algorithms as a 'Ifront e n d to traditional rule induction systems 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 difJicult texture classification problems. The results are encouraging and indicate significant advantages to the presented approach in this domain.

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This paper describes a hybrid methodology that integrates genetic algorithms (GAs) and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system.

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