Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries

• Published in: Biomimetics (Basel, Switzerland) Vol. 9; no. 1; p. 1
• Main Authors: Mengash, Hanan Abdullah, Alruwais, Nuha, Kouki, Fadoua, Singla, Chinu, Abd Elhameed, Elmouez Samir, Mahmud, Ahmed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-01-2024
• Subjects: Algorithms; Analysis; Artificial intelligence; bio-inspired algorithms; churn prediction; Communications industry; Customer relationship management; Customer retention; Customers; Decision making; Deep learning; Efficiency; Evaluation; Feature selection; Information management; Innovations; Machine learning; Marketing; Mathematical optimization; metaheuristics; Neural networks; Optimization algorithms; Prediction models; Social networks; telecom industry; Telecommunications industry; Telecommunications services industry; Saudi Arabia; telecom industry; metaheuristics; bio-inspired algorithms; feature selection; churn prediction
• ISSN: 2313-7673; 2313-7673
• DOI: 10.3390/biomimetics9010001

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%.