Development of proposed ensemble model for spam e-mail classification

Abstract

Spam e-mail documents classification is a very challenging task for e-mail users, especially non IT users. Billions of people using the internet and face the problem of spam e-mails. The automatic identification and classification of spam e-mails help to reduce the problem of e-mail users in managing a large amount of e-mails. This work aims to do a significant contribution by building a robust model for classification of spam e-mail documents using data mining techniques. In this paper, we use Enorn1 data set which consists of spam and ham documents collected from Kaggle repository. We propose an Ensemble Model-1 that is an ensemble of Multilayer Perceptron (MLP), Naïve Bayes and Random Forest (RF) to obtain better accuracy for the classification of spam and hame-mail docu-ments. Experimental results reveal that the proposed Ensemble Model-1 outperforms other existing classifiers as well as other proposed ensemble models in terms of classification accuracy. The suggested and proposed Ensemble Model-1 produces a high accuracy of 97.25% for classification of spam e-mail documents.