Filtering Junk E-Mail: A Performance Comparison between Genetic Programming and Naive Bayes

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Unpublished{katirai99,

• author = "Hooman Katirai",
• title = "Filtering Junk E-Mail: A Performance Comparison between Genetic Programming and Naive Bayes",
• year = "1999",
• month = "10 " # sep,
• note = "4A Year student project",
• URL = "http://www.mit.edu/~hooman/papers/katirai99filtering.pdf",
• URL = "http://citeseer.nj.nec.com/katirai99filtering.html",
• URL = "http://citeseer.ist.psu.edu/310632.html",
• keywords = "genetic algorithms, genetic programming, digital communications, spam, UBE",
• abstract = "This paper describes the application of genetic programming as a novel approach to the problem of filtering junk e-mail. We benchmark our results against the common standard: the naive Bayes classifier. While the genetically programmed classifier demonstrated a precision comparable to that of naive Bayes, it was slightly outperformed in recall. Since both learning methods gave similar results, it is recommended that a larger study be undertaken to ascertain whether these differences are indeed statistically significant. Further it is recommended that the performance of these classifiers be tested in a richer feature space more typical of real-world classifiers. Although the genetically programming classifier greatly outperformed the naive Bayes classifier in speed, it is concluded that a more efficient implementation of naive Bayes needs to be used in order to provide a fair comparison. We show that when left unabated, e-mail signatures also known as taglines reduce the value of several important features in junk e-mail detection; however it is also shown that these e-mail signatures may be harvested as advantageous features if some of their components are removed and noted as a feature. We therefore recommend that a better parser capable of meeting this criteria be implemented. To aid the reader in the theoretical aspects of our work, we have included introductory background for both approaches, including a full derivation of the generative naive Bayes model.",
• size = "27 pages",

}

Genetic Programming entries for Hooman Katirai

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