A Novel Feature Selection Method Using CFS with Tabu Search Algorithm in E-mail Spam Filtering

The purpose of this research is presenting an machine learning approach for enhancing the accuracy of automatic spam detecting and filtering and separating them from legitimate messages. In this regard, 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 research has used Correlation-based feature selection (CFS) with Tabu search algorithm. In addition, Multinomial Naïve Bayes (MNB) classifier, Discriminative Multinomial Naïve Bayes (DMNB) classifier, Support Vector Machine (SVM) classifier and Random Forest classifier are used for classification. Finally, the output results of this classifiers methods are examined and the best design is selected and it is compared with another similar works by considering different parameters. The optimal accuracy of the proposed system is evaluated equal to 99%.