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.