Abstract:

A Novel Spam Filtering Method (NSFM) is proposed, which removes redundant attributes from the high dimensional attributes, and selects the attributes, which contribute to the classification accuracy, thus, to improve the classification rate of spam filtering. A fuzzy adaptive particle swarm algorithm is developed, which can dynamically control the inertia weight, learning factor and particle number factor using fuzzy control. The NSFM consists of three stages, kernel feature selection, feature selection and spam filtering. In the first stage, information gain is employed to calculate the information value of each feature, and construct a kernel feature set, thereby obtaining a number of kernel feature subsets. In the second stage, according to the kernel feature subset, IFAPSO is initialized and adjusted adaptively using the fuzzy controller, thus finishing spam filtering. In the final stage, support vector machine is used to classify the optimal feature subset and finish spam filtering. In this paper, PUl, LingSpam and SpamAssassin data sets are utilized. Through many comparative experiments, it is confirmed that the proposed method is adaptable and can select better feature subsets, thereby enhancing the classification accuracy rate effectively, and building up the performance of spam filtering. The NSFM has important practical value.

Key words: artificial intelligence, feature selection, particle swarm optimization, fuzzy control, spam filtering, support vector machines