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

Title

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

Creator

Ghosh M.; Prabu P.

Description

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. 2019 Institute of Advanced Engineering and Science. All rights reserved.

Source

International Journal of Electrical and Computer Engineering, Vol-9, No. 4, pp. 3108-3114.

Date

2019-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Ensemble method; Machine Learning; Random Forest; Robocalls; XGBoost

Coverage

Ghosh M., Department of Computer Science, Christ (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India; Prabu P., Department of Computer Science, Christ (Deemed to Be University), Hosur Main Road, Bangalore, 560029, India

Rights

All Open Access; Gold Open Access; Green Open Access

Relation

ISSN: 20888708

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijece.v9i4.pp3108-3114

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