Using a probable weight based Bayesian approach for spam filtering

In the digital world that we live in today, Internet has merged and become an integral part of our life. It is difficult to imagine a life where there is no Internet or no email for that matter. Internet and email has resulted in disposal of huge amounts of information at everyone´s footsteps. Advent of powerful search engines such as Google® has revolutionized searches. Despite all this development, typically whenever we need something, we are presented with hundreds if not thousands of potential answers. Things seem to have gone out of control. In such a scenario, other sources of information such as online journals, emails, online newspapers and an online equivalent of just about anything and everything on paper, does not help the cause. Information has become so abundant, that we have difficulty in extracting the right and correct amount required for decision-making. This problem has been dubbed as the information overload, or too much information. This paper expects to resolve one aspect of this problem namely intelligently filtering out spam. The intelligent spam filter derives its intelligence using a combination of mathematical weights assigned to individual words appearing in each mail combined using Bayesian rule. This algorithm has achieved an average accuracy of 93 percent.