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 …