An　algorithm　combining　multiple　Naive　Bayesian　(NB)　filters　based　on　Gaussian　mixture　model　(GMM)　is　presented,　which　has　been　successfully　applied　to　e-mail　filtering.　The　method　uses　the　multiple　variates　statistics　analysis　to　model　the　relationship　between　the　training　data　set　and　their　classification　by　a　collection　of　NB　filters.　Then　a　GMM　can　be　learned　from　the　resulting　representation.　The　GMM　filters　previously　unseen　e-mails　according　to　the　principle　of　minimizing　expected-error-cost,　in　order　to　avoid　deleting　useful　e-mails.　Experimental　results　confirm　the　validity　of　our　method,　and　show　that　our　approach　is　insensitive　to　ratio　of　feature　subset　selection.