University of Hertfordshire

By the same authors

Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction

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Original languageEnglish
Article numbere1031
Number of pages21
JournalMathematics
Volume10
Issue7
Early online date23 Mar 2022
DOIs
Publication statusPublished - 23 Mar 2022

Abstract

The telecommunications industry is greatly concerned about customer churn due to dissatisfaction with service. This industry has started investing in the development of machine learning (ML) models for churn prediction to extract, examine and visualize their customers’ historical information from a vast amount of big data which will assist to further understand customer needs and take appropriate actions to control customer churn. However, the high-dimensionality of the data has a large influence on the performance of the ML model, so feature selection (FS) has been applied since it is a primary preprocessing step. It improves the ML model’s performance by selecting salient features while reducing the computational time, which can assist this sector in building effective prediction models. This paper proposes a new FS approach ACO-RSA, that combines two metaheuristic algorithms (MAs), namely, ant colony optimization (ACO) and reptile search algorithm (RSA). In the developed ACO-RSA approach, an ACO and RSA are integrated to choose an important subset of features for churn prediction. The ACO-RSA approach is evaluated on seven open-source customer churn prediction datasets, ten CEC 2019 test functions, and its performance is compared to particle swarm optimization (PSO), multi verse optimizer (MVO) and grey wolf optimizer (GWO), standard ACO and standard RSA. According to the results along with statistical analysis, ACO-RSA is an effective and superior approach compared to other competitor algorithms on most datasets.

Notes

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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