A GA-based RBF classifier with class-dependent features
Xiuju Fu, Lipo Wang · 2003
High dimensionality of data sets is a curse to classifiers. We propose to construct a novel radial basis function (RBF) classifier using class-dependent features by genetic algorithms (GA). Since each feature may have different capabilities in discriminating different classes, features should be masked differently for different classes. In our novel RBF classifier, each Gaussian kernel function of the RBF neural network is active for only a subset of patterns which are approximately of the same class. A group of Gaussian kernel functions is generated for each class. 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. Simulations show that, with irrelevant features removed for each class, our method can lead to significant improvements on classification accuracy.