In this paper, we report our work on spam filtering with three novel Bayesian classification methods: aggregating one-dependence estimators (AODE), hidden Naive Bayes (HNB), locally weighted learning with Naive Bayes (LWNB). Other four traditional classifiers: Naive Bayes, k nearest neighbor (kNN), support vector machine (SVM), C4.5 are also performed for comparison. Four feature selection methods: gain ratio, information gain, symmetrical uncertainty and ReliefF, are used to select relevant words for spam filtering. Results of experiments on two corpora show the promising capabilities of Bayesian classifiers for spam filtering, especial for that of AODE.