Comparing methods for multi-class probabilities in medical decision making using LS-SVMs and kernel logistic regression

Abstract

In this paper we compare thirteen different methods to obtain multi-class probability estimates in view of two medical case studies. The basic classification method used to implement all methods are least squares support vector machine (LS-SVM) classifiers. Results indicate that multi-class kernel logistic regression performs very well, together with a method based on ensembles of nested dichotomies. Also, a Bayesian LS-SVM method imposing sparseness performed very well for methods that combine binary probabilities into multi-class probabilities. © Springer-Verlag Berlin Heidelberg 2007.