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. Billionsof people using the internet and face the problem of spam e-mails. The automatic identification and classificationof spam e-mails help to reduce the problem of e-mail users in managing a large amount of e-mails. This work aimsto do a significant contribution by building a robust model for classification of spam e-mail documents using datamining techniques. In this paper, we use Enorn1 data set which consists of spam and ham documents collectedfrom 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 documents.Experimental results reveal that the proposed Ensemble Model-1 outperforms other existing classifiers aswell as other proposed ensemble models in terms of classification accuracy. The suggested and proposed EnsembleModel-1 produces a high accuracy of 97.25% for classification of spam e-mail documents.