Automatic thesaurus construction for spam filtering using revised back propagation neural network

Email has become one of the fastest and most economical forms of communication. Email is also one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. This paper proposes a new spam filtering system using revised back propagation (RBP) neural network and automatic thesaurus construction. The conventional back propagation (BP) neural network has slow learning speed and is prone to trap into a local minimum, so it will lead to poor performance and efficiency. The authors present in this paper the RBP neural network to overcome the limitations of the conventional BP neural network. A well constructed thesaurus has been recognized as a valuable tool in the effective operation of text classification, it can also overcome the problems in keyword-based spam filters which ignore the relationship between words. The authors conduct the experiments on Ling-Spam corpus. Experimental results show that the proposed spam filtering system is able to achieve higher performance, especially for the combination of RBP neural network and automatic thesaurus construction.