International Journal of Computer Science and Network Solutions (IJCSNS)
In this paper, the authors present a machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this paper, for reducing the error rate and increasing the efficiency, a new architecture on feature selection has been used. Features used in these systems, are the body of text messages. Proposed system of this paper has used Correlation-based Feature Selection (CFS) with Tabu search algorithm. In addition, Multinomial Naive Bayes (MNB) classifier, Discriminative Multinomial Naive Bayes (DMNB) classifier, Support Vector Machine (SVM) classifier and random forest classifier are used for classification.