This paper introduces an algorithm that combines nay�ve Bayes classification with feature weighting. Most of the related approaches to feature transformation for nay�ve Bayes suggest various heuristics and non-exhaustive search strategies for selecting a subset of features with which nay�ve 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 nay�ve 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.