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- W1904666 abstract "Nowadays e-mail has become a fast and economical way to exchange information. However, unsolicited or junk e-mail also known as spam quickly became a major problem on the Internet and keeping users away from them becomes one of the most important research area. Indeed, spam filtering is used to prevent access to undesirable e-mails. In this paper we propose a spam detection system called “3CA&1NB” which uses machine learning to detect spam. “3CA&1NB” has the characteristic of combining three cellular automata and one naive Bayes algorithm. We discuss how the combination learning based methods can improve detection performances. Our preliminary results show that it can detect spam effectively." @default.
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- W1904666 date "2012-01-01" @default.
- W1904666 modified "2023-10-01" @default.
- W1904666 title "Combining Classifiers for Spam Detection" @default.
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- W1904666 doi "https://doi.org/10.1007/978-3-642-30507-8_8" @default.
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