Identifying Valid Email Spam Emails Using Decision Tree

Abstract

The increasing use of e-mail and the growing trend of Internet users sending unsolicited bulk e-mail, the need for an anti-spam filtering or have created, Filter large poster have been produced in this area, each with its own method and some parameters are to recognize spam. The advantage of this method is the simultaneous use of two algorithms decision tree ID3-Mamdani and Naive Bayesian is fuzzy. The first two algorithms are then used to detect spam Bagging approach is to identify spam. In the evaluation of this dataset contains a thousand letters have been analyzed by the software Weka charts provided in spam detection accuracy than previous methods of improvement.