Variable Thresholding In Naïve Bayesian Spam Filters
نویسنده
چکیده
Email has become an essential means of communication for both business and personal use. However, the proliferation of unwanted email advertising or spam has cost organizations millions of dollars and has reduced the effectiveness of email as a communications medium. Recently, spam filters have been widely adopted as a means of combating these unwanted messages. This paper presents a method for better spam detection by combining the classical naïve Bayesian filter with a neural network that analyzes various characteristics of the email body. The results are analyzed and the method deemed effective in conditions where very strong thresholds must be set or where the training data is not exhaustive.
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