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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Islam, R. Wanlei Zhou |
| Copyright Year | 2007 |
| Description | Author affiliation: Deakin Univ., Melbourne (Islam, R.; Wanlei Zhou) |
| Abstract | This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques. |
| Starting Page | 51 |
| Ending Page | 58 |
| File Size | 604671 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769530494 |
| DOI | 10.1109/PDCAT.2007.71 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-12-03 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electronic mail Machine learning Classification algorithms Humans Costs Feedback Internet Distributed computing Information technology Filtering |
| Content Type | Text |
| Resource Type | Article |
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