A performance study of Gaussian kernel classifiers for data mining applications

Radial basis function (RBF) models have been successfully employed to study a broad range of data mining problems and benchmark data sets for real world scientific and engineering applications. In this paper we investigate RBF models with Gaussian kernels by developing classifiers in a systematic way. In particular, we employ our newly developed RBF design algorithm for a detailed performance study and sensitivity analysis of the classification models for the popular Monk's problems. The results show that the accuracy of our classifiers is very impressive while our classification approach is systematic and easy to implement. In addition, differing complexity of the three Monk's problems is clearly reflected in the classification error surfaces for test data. By exploring these surfaces, we acquire better understanding of the data mining classification problems. Finally, we study the error surfaces to investigate trade-offs between different choices of model parameters to develop efficient and parsimonious models for a given application.