Bayesian Chinese Spam Filter Based on Crossed N-gram

Naive Bayesian spam email filters are a wellknown and powerful type of filters that can easily be induced from a dataset of sample cases. However, the problem of segmenting words for Chinese email restricts its performance. In this paper, we present a Bayesian Chinese spam filter based on cross N-gram. This method does not need to carry on segmenting words for Chinese emails, so that it can avoid to be restricted by inaccurate words segmenting. It also needn¿t to install segmenting word dictionary and is easy to install in the user terminal to construct an individualized spam filter since the space and time efficiency are improved. The restriction on independence assumption of naive bayes method is relaxed in some degree. The results of experiments show that the proposed method can acquire a high accuracy ratio at low cost.