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Taghavifard, Mohammad Taghi, Rezasoltani, Arman, Mahmoudi, Mahsa, salehi moghadam, shima, Khani, Amir Mohammad. (1404). A Hybrid Machine Learning Model Optimized with Reinforcement Learning–Enhanced Spider Wasp Optimizer for Customer Value Prediction. نشریه علمی دانشگاه سیستان و بلوچستان, 17(2), 203-229. doi: 10.22111/ijbds.2026.53633.2294
Mohammad Taghi Taghavifard; Arman Rezasoltani; Mahsa Mahmoudi; shima salehi moghadam; Amir Mohammad Khani. "A Hybrid Machine Learning Model Optimized with Reinforcement Learning–Enhanced Spider Wasp Optimizer for Customer Value Prediction". نشریه علمی دانشگاه سیستان و بلوچستان, 17, 2, 1404, 203-229. doi: 10.22111/ijbds.2026.53633.2294
Taghavifard, Mohammad Taghi, Rezasoltani, Arman, Mahmoudi, Mahsa, salehi moghadam, shima, Khani, Amir Mohammad. (1404). 'A Hybrid Machine Learning Model Optimized with Reinforcement Learning–Enhanced Spider Wasp Optimizer for Customer Value Prediction', نشریه علمی دانشگاه سیستان و بلوچستان, 17(2), pp. 203-229. doi: 10.22111/ijbds.2026.53633.2294
Taghavifard, Mohammad Taghi, Rezasoltani, Arman, Mahmoudi, Mahsa, salehi moghadam, shima, Khani, Amir Mohammad. A Hybrid Machine Learning Model Optimized with Reinforcement Learning–Enhanced Spider Wasp Optimizer for Customer Value Prediction. نشریه علمی دانشگاه سیستان و بلوچستان, 1404; 17(2): 203-229. doi: 10.22111/ijbds.2026.53633.2294

A Hybrid Machine Learning Model Optimized with Reinforcement Learning–Enhanced Spider Wasp Optimizer for Customer Value Prediction

International Journal of Business and Development Studies
مقاله 10، دوره 17، شماره 2، دی 2025، صفحه 203-229    اصل مقاله (1.03 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijbds.2026.53633.2294
نویسندگان
Mohammad Taghi Taghavifard1؛ Arman Rezasoltani2؛ Mahsa Mahmoudi3؛ shima salehi moghadam4؛ Amir Mohammad Khani* 2
1Professor, Department of Information Technology and Operations Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
2Ph.D. Candidate, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
3Department of Management, Faculty of Administrative Sciences and Economics,University of Isfahan, Isfahan, Iran.
4Department of Management, Allameh Askari International University, Tehran, Iran.
چکیده
Having an accurate estimation of a customer's worth is one of the more important tasks performed by banks in this modern world, especially with the profound number of customers and the complex nature of transactions, along with the massive variance in transactions. In light of this need, we develop a multi-layer stacked ensemble model specifically designed to improve the predictive performance of banking customers in Iran. The first layer consists of 4 different learners (XGBoost, CatBoost, Random Forest, and Gradient Boosting). Each model has its learning capacity and learns from customer behavior and financial characteristics in complementary ways. The second layer consists of a LightGBM classifier, which fuses (by meta-model) the outputs of the first-layer learners into the final prediction. The second set of model hyperparameters were optimized using a Reinforcement Learning (RL)-based SWO to efficiently search for optimal hyperparameters across a high-dimensional space, which is typically not well-explored using classic optimization strategies. Utilizing a repeated 5-fold stratified cross-validation approach, we were able to achieve strong predictive accuracy: Accuracy = 89.70%; Precision = 92.84%; Recall = 92.46%; F-Score = 92.61%; ROC AUC = 0.9632; all of which surpass the single models. Our results provide evidence supporting the successful application of a multi-layer ensemble with metaheuristic hyperparameter optimization in building a viable and powerful customer valuation tool for banks.
کلیدواژه‌ها
Ensemble Learning؛ Customer Value Prediction؛ Stacking Model؛ Machine Learning in Banking؛ Multi-Layer Classification Models
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