Rule extraction from an RBF classifier based on class-dependent features

Rule extraction is a technique for knowledge discovery. Compact rules with high accuracy are desirable. Due to the curse of irrelevant features to classifiers, feature selection techniques are discussed widely. We propose to extract rules based on class-dependent features from a radial basis function (RBF) classifier by genetic algorithms (GA). Each Gaussian kernel function of the RBF neural network is active for only a subset of patterns which are approximately of the same class. Since each feature may have different capabilities in discriminating different classes, features should be masked differently for different classes. In our method, different feature masks are used for different groups of Gaussian kernel functions corresponding to different classes. The feature masks are adjusted by GA. The classification accuracy of the RBF neural network is used as the fitness function. Thus, the dimensionality of a data set is reduced. Concise rules with high accuracy are subsequently obtained based on the class-dependent features. We demonstrate our approach using computer simulations