This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classification. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. For a given feature subset, ID3 is invoked to produce a decision tree. The classification performance of the decision tree on unseen data is used as a measure of fitness for the given feature set, which, in turn, is used by the GA to evolve better feature sets. This GA-ID3 process iterates until a feature subset is found with satisfactory classification performance. Experimental results are presented which illustrate the feasibility of our approach on difficult problems involving recognizing visual concepts in satellite and facial image data. The results also show improved classification performance and reduced description complexity when compared against standard methods for feature selection.
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