Authors: R. Jensi
The ever increasing menace of spam is bringing down productivity. More than 70% of the email messages are spam, and it has become a challenge to separate such messages from the legitimate ones. I have developed a spam identification engine which employs naive Bayesian classifier to identify spam. A new concept-based mining model that analyzes terms on the sentence, document is introduced. . The concept-based mining model can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed mining model consists of sentence-based concept analysis, document-based concept analysis similarity measure. In this paper, a machine learning approach based on Bayesian analysis to filter spam is described. The filter learns how spam and non spam messages look like, and is capable of making a binary classification decision (spam or non-spam) whenever a new email message is presented to it. The evaluation of the filter showed its ability to make decisions with high accuracy. This cost sensitivity was incorporated into the spam engine and I have achieved high precision and recall, thereby reducing the false positive rates.
Comments: 4 Pages.
[v1] 2012-08-18 12:59:29
Unique-IP document downloads: 65 times
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.