The　growing　problem　of　unsolicited　bulk　e-mail,　also　known　as　＇spam＇,　has　generated　a　need　for　reliable　anti-spam　e-mail　filters.　We　introduce　seven　filtering　algorithms:　Naive　Bayesian　(NB),　Decision　Tree　(DT),　AdaBoost,　ANN,　SVM,　VSM　and　KNN.　Design　considerations　and　implementation　issues　of　these　filters　are　discussed,　such　as　how　to　get　cost-sensitive　NB,　SVM,　VSM,　KNN.　Using　two　relatively　large　amounts　of　real　personal　Email　data,　a　comprehensive　comparative　study　based　on　a　cost-sensitive　measure　we　approved　was　conducted　using　above　seven　filters.　The　study　includes　the　effect　of　feature　subset　size,　training-corpus　distribution,　issues　that　have　not　been　explored　in　previous　experiments.　The　comparative　results　show　that　cost-sensitive　filters　such　as　NB,　SVM,　VSM　and　KNN　have　fewer　count　of　misclassifying　legitimate　when　relative　parameters,　feature　subset　size　and　training　dataset＇s　distribution　are　reasonable.