The problem of junk mail, also called spam, has reached epic proportions and various efforts are underway to fight spam. Junk mail classification using machine learning techniques is a key method to fight spam. We have devised a machine learning algorithm where features are created from individual sentences in the subject and body of a message by forming all possible word-pairings from a sentence. Weights are assigned to the features based on the strength of their predictive capabilities for spam/legitimate determination. The predictive capabilities are estimated by the frequency of occurrence of the feature in spam/legitimate collections as well as by application of heuristic rules. During classification, total spam and legitimate evidence in the message is obtained by summing up the weights of extracted features of each class and the message is classified into whichever class accumulates the greater sum.
We compared the algorithm against the popular naïve-bayes algorithm (in [8]) and found it's performance exceeded that of naïve-bayes algorithm both in terms of catching spam and for reducing false positives.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-2144 |
| Date | 20 January 2003 |
| Creators | Malkhare, Rohan V |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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