Abstract:

It’s well known that the spam-short-messages are annoying cell-phone users and mobile service providers everyday. A new spam-short-messages filtering system, combining online filtering with offline classifying, is presented. The system can filter messages efficiently according to the sending behavior characteristics and the messages contents. Additionally, a self-learning mechanism is designed based on its operators’ feedback. It enables the classifiers of the system to improve themselves according to the filtering results. Compared with traditional methods, the presented method has better performance in terms of filtering efficiency and accuracy.

Key words: spam short message filtering, statistical learning, text categorization