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

Authors:
Ben Van Calster;Jan Luts;Johan A. K. Suykens;George Condous;Tom Bourne;Dirk Timmerman;Sabine Van Huffel
Affiliations:
Department of Electrical Engineering, ESAT-SISTA, Katholieke Universiteit Leuven, Leuven, Belgium;Department of Electrical Engineering, ESAT-SISTA, Katholieke Universiteit Leuven, Leuven, Belgium;Department of Electrical Engineering, ESAT-SISTA, Katholieke Universiteit Leuven, Leuven, Belgium;Nepean Hospital, University of Sydney, Sydney, Australia;St Georges Hospital Medical School, London, UK;University Hospitals K.U. Leuven, Leuven, Belgium;Department of Electrical Engineering, ESAT-SISTA, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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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.