DRC-BK: Mining classification rules by using Boolean Kernels

Abstract

An understandable classification models is very useful to human experts. Currently, SVM classifiers have good classification performance; however, their classification model is non-understandable. In this paper, we build DRC-BK, a decision rule classifier, which is based on structural risk minimization theory. Experiment results on UCI dataset and Reuters21578 dataset show that DRC-BK has excellent classification performance and excellent scalability, and that when applied with MPDNF kernel, DRC-BK performances the best. © Springer-Verlag Berlin Heidelberg 2005.