International Research Journal of Education and Technology

In this e-world, most of the transactions and business is taking place through e-mails. Nowadays, email becomes a powerful tool for communication as it saves a lot of time and cost. But, due to social networks and advertisers, most of the emails contain unwanted information called spam. Even though lot of algorithms has been developed for email spam classification, still none of the algorithms produces 100% accuracy in classifying spam emails. In this paper, spam dataset is analyzed using TANAGRA data mining tool to explore the efficient classifier for email spam classification. Initially, feature construction and feature selection is done to extract the relevant features. Then various classification algorithms are applied over this dataset and cross validation is done for each of these classifiers. Finally, best classifier for email spam is identified based on the error rate, precision and recall.

Key Words:  classifier, e-mail, feature construction, feature selection, relevance analysis, spam.