Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques
Subject Areas : Machine learning
mohammad sedighimanesh
1
*
,
Ali Sedighimanesh
2
,
Hessam Zandhessami
3
1 - Science and Research branch, Islamic Azad University
2 - Science and Research branch, Islamic Azad University
3 - Science and Research branch, Islamic Azad University
Keywords: Customer Churn Prediction, Hyperparameter Optimization, Particle Swarm Optimization (PSO), Deep Learning Models, Telecommunications Analytics,
Abstract :
Background: In today’s competitive market, predicting customer churn with high accuracy is crucial for enterprises to maintain growth and profitability. Traditional predictive models often lack in accuracy due to the complexity of customer behavior.
Objective: This research aims to improve the accuracy of predicting customer churn by utilizing the Particle Swarm Optimization (PSO) algorithm for optimizing the hyperparameters of a composite deep learning model. The performance of this enhanced model is evaluated against traditional models such as LSRM_GRU, LSTM, GRU, and CNN_LSTM to demonstrate the effectiveness of PSO in hyperparameter tuning.
Methods: A composite deep learning approach was employed, integrating various neural network architectures to leverage their strengths in modeling complex customer interactions. The PSO algorithm was used to optimize the model’s hyperparameters. Customer transaction and interaction data from different business operations served as the dataset for testing and analyzing the model’s performance. Evaluation metrics including accuracy, precision, recall and F1 score, and ROC AUC were utilized for a detailed comparison with established models.
Findings: The PSO-enhanced composite deep learning model showed superior performance across all metrics, significantly outperforming the LSRM_GRU, LSTM, GRU, and CNN_LSTM models. Notably, improvements in ROC AUC and F1 score highlight the robustness and balanced precision-recall trade-off of the proposed model, demonstrating its effectiveness in identifying potential churners.
Conclusion: The integration of PSO for hyperparameter optimization in composite deep learning models for customer churn prediction has proven to significantly enhance predictive accuracy and performance metrics over conventional models. This underscores the potential of evolutionary algorithms in improving deep learning applications.
[1] N. Jajam, N. P. Challa, K. S. L. Prasanna, and C. H. V. S. Deepthi, “Arithmetic Optimization With Ensemble Deep Learning SBLSTM-RNN-IGSA Model for Customer Churn Prediction,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3304669.
[2] S. W. Fujo, S. Subramanian, and M. A. Khder, “Customer churn prediction in telecommunication industry using deep learning,” Information Sciences Letters, vol. 11, no. 1, 2022, doi: 10.18576/isl/110120.
[3] A. Khattak, Z. Mehak, H. Ahmad, M. U. Asghar, M. Z. Asghar, and A. Khan, “Customer churn prediction using composite deep learning technique,” Sci Rep, vol. 13, no. 1, p. 17294, 2023.
[4] I. Ullah, B. Raza, A. K. Malik, M. Imran, S. U. Islam, and S. W. Kim, “A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2914999.
[5] S. A. Panimalar and A. Krishnakumar, “A review of churn prediction models using different machine learning and deep learning approaches in cloud environment,” Journal of Current Science and Technology, vol. 13, no. 1. 2023. doi: 10.14456/jcst.2023.12.
[6] L. Geiler, S. Affeldt, and M. Nadif, “A survey on machine learning methods for churn prediction,” International Journal of Data Science and Analytics, vol. 14, no. 3. 2022. doi: 10.1007/s41060-022-00312-5.
[7] S. De, P. Prabu, and J. Paulose, “Effective ML Techniques to Predict Customer Churn,” in Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021, 2021. doi: 10.1109/ICIRCA51532.2021.9544785.
[8] P. Gopal and N. Bin MohdNawi, “A Survey on Customer Churn Prediction using Machine Learning and data mining Techniques in E-commerce,” in 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, 2021. doi: 10.1109/CSDE53843.2021.9718460.
[9] M. Sadeghi, M. N. Dehkordi, B. Barekatain, and N. Khani, “Improve customer churn prediction through the proposed PCA-PSO-K means algorithm in the communication industry,” Journal of Supercomputing, vol. 79, no. 6, 2023, doi: 10.1007/s11227-022-04907-4.
[10] J. Vijaya and E. Sivasankar, “An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing,” Cluster Comput, vol. 22, 2019, doi: 10.1007/s10586-017-1172-1.
[11] I. Al-Shourbaji, N. Helian, Y. Sun, S. Alshathri, and M. A. Elaziz, “Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction,” Mathematics, vol. 10, no. 7, 2022, doi: 10.3390/math10071031.
[12] A. Idris, A. Iftikhar, and Z. ur Rehman, “Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling,” Cluster Comput, vol. 22, 2019, doi: 10.1007/s10586-017-1154-3.
[13] A. Dalli, “Impact of Hyperparameters on Deep Learning Model for Customer Churn Prediction in Telecommunication Sector,” Math Probl Eng, vol. 2022, 2022, doi: 10.1155/2022/4720539.
[14] Y. Zhang, S. Wang, and G. Ji, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications,” Mathematical Problems in Engineering, vol. 2015. 2015. doi: 10.1155/2015/931256.
[15] M. N. Ab Wahab, S. Nefti-Meziani, and A. Atyabi, “A comprehensive review of swarm optimization algorithms,” PLoS One, vol. 10, no. 5, 2015, doi: 10.1371/journal.pone.0122827.
[16] J. Fang, W. Liu, L. Chen, S. Lauria, A. Miron, and X. Liu, “A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization,” International Journal of Network Dynamics and Intelligence, 2023, doi: 10.53941/ijndi0201002.
[17] S. Agrawal, A. Das, A. Gaikwad, and S. Dhage, “Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning,” in 2018 International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2018, 2018. doi: 10.1109/ICSCEE.2018.8538420.
[18] A. Amin, F. Al-Obeidat, B. Shah, A. Adnan, J. Loo, and S. Anwar, “Customer churn prediction in telecommunication industry using data certainty,” J Bus Res, vol. 94, 2019, doi: 10.1016/j.jbusres.2018.03.003.
[19] N. I. Mohammad, S. A. Ismail, M. N. Kama, O. M. Yusop, and A. Azmi, “Customer Churn Prediction in Telecommunication Industry Using Machine Learning Classifiers,” in ACM International Conference Proceeding Series, 2019. doi: 10.1145/3387168.3387219.