Tweaking naive Bayes classifier for intelligent spam detection
2011
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¨ive 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.
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