Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction

Ibrahim Al-Shourbaji, Na Helian, Yi Sun, Samah Alshathri and Mohamed Abd Elaziz
Additional contact information
Ibrahim Al-Shourbaji: Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Na Helian: Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Yi Sun: Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Samah Alshathri: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohamed Abd Elaziz: Faculty of Science & Engineering, Galala University, Suze 435611, Egypt

Mathematics, 2022, vol. 10, issue 7, 1-21

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.

Keywords: feature selection; machine learning; metaheuristic algorithms; ant colony optimization; reptile search algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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