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An efficient churn ...
An efficient churn prediction model using gradient boosting machine and metaheuristic optimization
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- Alshourbaji, Ibrahim (author)
- Univ Hertfordshire, England; Jazan Univ, Saudi Arabia
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- Helian, Na (author)
- Univ Hertfordshire, England
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- Sun, Yi (author)
- Univ Hertfordshire, England
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- Hussien, Abdelazim (author)
- Linköpings universitet,Programvara och system,Tekniska fakulteten,Fayoum Univ, Egypt
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- Abualigah, Laith (author)
- Al Al Bayt Univ, Jordan; Lebanese Amer Univ, Lebanon; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ, Malaysia
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- Elnaim, Bushra (author)
- Prince Sattam Bin Abdulaziz Univ, Saudi Arabia
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(creator_code:org_t)
- NATURE PORTFOLIO, 2023
- 2023
- English.
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In: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 13:1
- Related links:
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https://doi.org/10.1...
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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- Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBMs promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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