Research of spam-filtering based on optimized naive Bayesian algorithm
Xiaolong Ma · Jisuanji yingyong yanjiu · 2012
This paper discussed improvement of naive Bayesian text classification algorithms based on the SVM-EM algorithms and applications in spam filtering.Naive Bayes algorithm cannot handle the results based on the feature-based combination changes feature-based,and dependent on the distribution of sample space and the inherent instability of the defect,causing the algorithm complexity increases.To solve the above problems,this paper proposed an improved algorithm based on SVM-EM naive Bayes algorithm,which was combined with naive Bayes algorithm's simple and efficient,the advantages of filling the missing property of EM,the advantages of support vector machines(SVM) algorithms,first made nonlinear transformation and structural risk minimization flow into the second classification optimization problem,and then asked the EM algorithm to fill the requirements of the conditional independence assumptions for Bayesian algorithm.Finally,using Bayesian algorithms to improve the mail filtering classification accuracy and stability.Simulation results show that the proposed method can quickly obtain the optimal feature subset classification,greatly improve the spam filtering accuracy and stability compared to traditional methods of mail filtering algorithm.