Abstract:

This paper proposes a personal spam email filtering method, which can learn a user’s interests and update it automatically according to the user’s feedback. The proposed method extracts the linguistic features and behavior ones to build some rulebased individual filters, and uses the SVM ensemble learning method to combine the multifilter’s results. Applying an active learning method to choose those knowledgeable emails with the user’s labels, the method can minimize the number of labeled emails and reach steadystate performance more quickly. The experimental results show the personal filtering method based on ensemble learning and active learning can capture personality, and achieve high performance with the considerations on accuracy, efficiency and learning ability.

Key words: spam email filtering;personal;ensemble learning;active learning;SVM