A support vector machine based naive Bayes algorithm for spam filtering
Naive Bayes classifiers are widely used to filter spam emails, however, the strong independence assumptions between features limit their performance in accurately identifying spams. To address this issue, we proposed a support machine vector based naive Bayes SVM NB filtering system. The SVM NB first constructs an optimal separating hyperplane that divides samples in the training set into two categories. For samples located nearby the hyperplane, if they are in different categories, one of them will be eliminated from the training set. In this way, the dependence between samples is reduced and the entire training sample space is simplified. With the trimmed training set, the naive Bayes algorithm is applied to classify emails in the test set. The SVM NB system is evaluated with the dataset obtained from DATAMALL. Experiment results demonstrate that SVM NB can achieve a higher spam detection accuracy and a faster classification speed.
2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC)