Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing E-mail Classifier
Akhilesh Kumar Shrivas · 2013
Due to increased bandwidth and strong infrastructure available for accessing internet, internet users are growing rapidly. Internet users frequently use e-mail for fast data communication of audio, vedio and textual data but at the same time they are facing problem due to unwanted e- mail known as spam e-mail. In order to filter this unwanted e-mail, a classifier must be placed in the network or in computer. In this paper three different types of technique: Artificial Neural Network (ANN), Decision tree and statistical technique are explored for designing and developing e-mail classifier. Experimental work has been performed on e-mail data set obtained from UCI repository site and is partitioned into three different partitions to find out best suitable partition to be applied for various model. A suitable ensemble model is chosen based on various error measures calculated after training and testing the models. A final ensemble model is measured in terms of accuracy, precision, recall, F-measure and Gain Chart. Highest accuracy of 94.35% is obtained in case of ensemble of C5.0 and SVM with 60%-40% (training - testing) partition.