Machine learning approach for filtering spam emails

Abstract

An efficient email spam filtering system by selecting relevant features to reduce the dimensions has become a pivotal aspect in the field of machine learning based spam filtering. To deal with noisy features, TF-IDF-CF is chosen as the feature selection method in this study. The selected relevant feature sets are submitted to LibSVM and MNB classifiers to construct ham and spam models. An accuracy of 98.2612 with F-measure 0.9841 is obtained which depicts the effectiveness of proposed scheme.