Probabilistic anti-spam filtering with dimensionality reduction
Proceedings of the 2010 ACM Symposium on Applied Computing
Filtering spams using the minimum description length principle
Proceedings of the 2010 ACM Symposium on Applied Computing
Contributions to the study of SMS spam filtering: new collection and results
Proceedings of the 11th ACM symposium on Document engineering
Detection of near-duplicate user generated contents: the SMS spam collection
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Facing the spammers: A very effective approach to avoid junk e-mails
Expert Systems with Applications: An International Journal
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There are different approaches able to automatically detect e-mail spam messages, and the best-known ones are based on Bayesian decision theory. However, the most of these approaches have the same difficulty: the high dimensionality of the feature space. Many term selection methods have been proposed in the literature. Nevertheless, it is still unclear how the performance of naive Bayes anti-spam filters depends on the methods applied for reducing the dimensionality of the feature space. In this paper, we compare the performance of most popular methods used as term selection techniques, such as document frequency, information gain, mutual information, X 2 statistic, and odds ratio used for reducing the dimensionality of the term space with four well-known different versions of naive Bayes spam filter.