关键词: 垃圾邮件过滤, 贝叶斯分类, 数据平滑

Abstract: When applied to deal with Spam Filter task, Na?ve Bayes almost suffers from the sparse data problem. Moreover, this problem is hardly to be solved by expanding the corpora, since the distribution of features in the corpora complies with the Zipf’s law. Three aspects of work are done to alleviate the above problem in this paper. Firstly, a smoothing algorithm is adopted and embedded into Na?ve Bayes to estimate the compensation probability of unseen feature. Secondly, domain term extraction and semantic knowledge are introduced in the Spam Filter model to enhance the performance of semantic process. Thirdly, an incremental learning method is introduced to perform the iterative learning. The experimental corpora comes from the Ling-Spam, and the result of open test shows that this method increases the precision by 2.51%. In addition, the experiment in National 863 Evaluation on Text Classification shows that the Na?ve Bayes performance with Good-Turing algorithm is 3.05% higher than that with Laplace.

Key words: spam filter, Na?ve Bayes classification, data smoothing