Implementing Multi-class Classifiers by One-class Classification Methods

In this paper we address the problem of how to implement a multi-class classifier by an ensemble of one-class classifiers. One-class classifiers are first trained for each class and then a decision function is formulated based on minimum distance rules. Two kinds of one-class classifiers are explored: the support vector domain description and a kernel principle component analysis based method. Both of the two methods can work in the feature space and deal with nonlinear classification problems. Experiments on some benchmark datasets show that the proposed methods with carefully tuned parameters have comparable generalization ability with support vector machines while having some other advantages.