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Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction

Author

Listed:
  • Ibrahim Al-Shourbaji

    (Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
    Department of Computer and Network Engineering, Jazan University, Jazan 82822-6649, Saudi Arabia)

  • 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
    Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

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.

Suggested Citation

  • Ibrahim Al-Shourbaji & Na Helian & Yi Sun & Samah Alshathri & Mohamed Abd Elaziz, 2022. "Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1031-:d:778063
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    References listed on IDEAS

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    1. Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
    2. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    3. Jinjin Ding & Qunjin Wang & Qian Zhang & Qiubo Ye & Yuan Ma, 2019. "A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, March.
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    Cited by:

    1. Ibrahim Al-Shourbaji & Pramod H. Kachare & Samah Alshathri & Salahaldeen Duraibi & Bushra Elnaim & Mohamed Abd Elaziz, 2022. "An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection," Mathematics, MDPI, vol. 10(13), pages 1-20, July.

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