Empirical analysis of ensemble methods for the classification of robocalls in telecommunications

Abstract

With the advent of technology, there has been an excessive use of cellular phones. Cellular phones have made life convenient in our society. However, individuals and groups have subverted the telecommunication devices to deceive unwary victims. Robocalls are quite prevalent these days and they can either be legal or used by scammers to trick one out of their money. The proposed methodology in the paper is to experiment two ensemble models on the dataset acquired from the Federal Trade Commission (DNC Dataset). It is imperative to analyze the call records and based on the patterns the calls can classify as a robocall or not a robocall. Two algorithms Random Forest and XgBoost are combined in two ways and compared in the paper in terms of accuracy, sensitivity and the time taken.