A Method Combined N-gram Based to Filter the Chinese Spam
Xinbin Liu · Microelectronics & Computer · 2004
The situation that mailbox is nowadays flooded with spam in China asks urgently for a technical solution to stop them. Many researches indicate that text classification is a feasible way. A Naive Bayesian Algorithm is proposed in this paper to model the filtering and a N-gram method is also introduced to segment the Chinese text into word. Measures have been taken to classify the cost-asymmetrical problem. Values of several parameters, namely TCR (total cost ratio), SR (spam recall) and SP (spam precision), are also applied to evaluate the cost sensitivity. Results of experiments show that the proposed model can acquire a high accuracy ratio at a low cost. Thus, we can conclude that sifting the training mail corpus carefully can improve the performance, so as to meet the requirements of Isp-level application.