Research of a spam filter based on improved naive Bayes algorithm - 曹翠玲; 王媛媛; 袁野; 赵国冬 (网络与信息安全学报)
In spam filtering filed,naive Bayes algorithm is one of the most popular algorithm,a modified using support vector machine(SVM)of the native Bayes algorithm :SVM-NB was proposed.Firstly,SVM constructs an optimal separating hyperplane for training set in the sample space at the junction two types of collection,Secondly,according to its similarities and differences between the neighboring class mark for each sample to reduce the sample space also increase the independence of classes of each samples.Finally,using naive Bayesian classification algorithm for mails.The simulation results show that the algorithm reduces the sample space complexity,get the optimal classification feature subset fast,improve the classification speed and accuracy of spam filtering effectively.