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Tweaking naive Bayes classifier for intelligent spam detection

Raturi, Ankita and Lal, Sunil P. (2011) Tweaking naive Bayes classifier for intelligent spam detection. [Conference Proceedings]

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    Abstract

    Spam classification is a text classification task that is commonly implemented using Bayesian learning. These classification methods are often modified in order to improve the accuracy and minimize false positives. This paper describes a Na¨ıve Bayes (NB) classifier for basic spam classification. This is then augmented with a cascaded filter that uses a Weighted-Radial Bias Function (W-RBF) for similarity measure. It is expected that the NB classifier will perform the basic classification with the W-RBF acting as a secondary filter, thus improving the performance of the spam classifier. It was found that the NB portion of the cascade was the initial spam filter with the W-RBF filter acting as a False Positive filter.

    Item Type: Conference Proceedings
    Subjects: Q Science > QA Mathematics
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Ms Shalni Sanjana
    Date Deposited: 25 Jun 2011 19:57
    Last Modified: 25 Jun 2012 19:57
    URI: http://repository.usp.ac.fj/id/eprint/4848
    UNSPECIFIED

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