On the effects of class noise on spam detection accuracy
Spam contributes to approximately two-thirds of the e-mail traffic over the Internet [9] and is fast becoming a major problem for IT users and network administrators. Spam costs billions in lost productivity [21] and results in more problems than mere annoyance of delayed and lost non-spam e-m...
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| Main Authors: | , , , |
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| Format: | Book Section |
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Penerbit UTM
2007
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/13679/ |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | Spam contributes to approximately two-thirds of the e-mail traffic over the Internet [9] and is fast becoming a major problem for IT users and network administrators. Spam costs billions in lost productivity [21] and results in more problems than mere annoyance of delayed and lost non-spam e-mails. Spam continuously evolves to circumvent spam control systems and is becoming much more sophisticated [13]. Naive Bayes classification has widely been used for spam detection and several variations have been proposed [1], [25], [11]. As other supervisedlearning techniques, its accuracy (for detecting spam) depends on the quality, quantity, and timeliness of the learning corpora |
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