Abstract
Wilson has reported results obtained by a genetic learning system Boole on a small, abstract learning task. This task is shown to have a property that complicates its analysis by top-down decision tree methods. Nevertheless, experiments with decision tree methods have shown that accurate classifiers can be obtained from comparatively small sets of training examples. Finally, conversion of the decision trees to sets of production rules has led to a significant improvement in classification accuracy for this task.
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