Machine Learning Techniques for Classification of Spambase Dataset: A Hybrid Approach

Abstract

Email has become a necessity for this new generation for official communication purposes. As the use of Internet is becoming more and more the risk of being caught into its darker side is so common. The major concern is spam, which is growing exponentially, and the users are becoming victim of it on daily basis. This paper proposes a hybrid machine learning classification model for the spam classification on the spambase dataset. This model uses the four classification algorithms namely Ensemble Classification, Decision Tree, Random Forest and Support Vector Machine (SVM). There are two phases; First phase deals with the classification of spambase dataset in two classes i.e. spam and ham with Decision Tree machine learning algorithm and the second phase comprises of classification improvisation of the output produced by phase one with four machine learning algorithms i.e. Decision Tree, Random Forest, Support Vector Machine (SVM) and Ensemble Learning. The experiment shows a very promising result with improvised accuracy in second phase.