WBC_SVM: Weighted Bayesian Classification based on Support Vector Machines

Thomas Gaertner and Peter A. Flach. In Carla E. Brodley and Andrea Pohoreckyj Danyluk, editors, Proceedings of the Eighteenth International Conference on Machine Learning, pages 207--209. Morgan Kaufmann, June 2001.

Abstract

This paper introduces an algorithm that combines nave Bayes classification with feature weighting. Most of the related approaches to feature transformation for nave Bayes suggest various heuristics and non-exhaustive search strategies for selecting a subset of features with which nave Bayes performs better than with the complete set of features. In contrast, the algorithm introduced in this paper employs feature weighting performed by a support vector machine. The weights are optimised such that the danger of overfitting is reduced. To the best of our knowledge, this is the first time that nave Bayes classification has been combined with feature weighting. Experimental results on 15 UCI domains demonstrate that WBCSVM compares favourably to state-of-the-art machine learning approaches.

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T Gaertner, gaertner@cs.bris.ac.uk,