a hybrid model for customer churn prediction: an optimized combination of multilayer perceptron and atomic orbital search

Customer churn is a formidable and persistent challenge for the telecommunications sector, as it significantly erodes profitability and impedes sustainable competitive advantage. conventional machine learning methods often exhibit limitations in capturing non-linear intricacies and effectively managing voluminous, high-dimensional datasets. to address these issues, this study presents a novel hybrid predictive framework for enhanced customer churn prediction. our proposed methodology integrates an optimized multilayer perceptron (mlp) with the atomic orbital search (aos) metaheuristic algorithm. crucially, aos is systematically deployed to fine-tune the critical connection weights and biases of the mlp architecture, mitigating issues such as premature convergence and suboptimal parameter selection that are common in standalone neural networks. empirical validation, conducted using the comprehensive teldata dataset (7043 instances, 20 features) in matlab, unequivocally demonstrates the hybrid approach's superior efficacy over established conventional methodologies. specifically, the hybrid mlp-aos model achieved an impressive predictive accuracy of 97.8% on the test subset, a notable improvement over the 79% achieved by support vector machines (svms) and the 75% achieved by a conventional mlp. these compelling findings underscore the proposed approach's ability to predict customer churn with heightened precision, providing telecommunications management with an analytical tool for identifying pivotal influencing factors and formulating effective retention strategies.