Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques
محورهای موضوعی : Machine learning
Mohammad Sedighimanesh
1
*
,
Ali Sedighimanesh
2
,
Hessam Zandhessami
3
1 - Independent Researcher, Specialist in Artificial Intelligence and Data Analysis, Tehran, Iran
2 - Independent Researcher, Specialist in Artificial Intelligence and Data Analysis, Tehran, Iran
3 - Department of Management and Economics, Science and Research branch, Islamic Azad University, Tehran, Iran
کلید واژه: Customer Churn Prediction, Hyperparameter Optimization, Particle Swarm Optimization (PSO), Deep Learning Models, Telecommunications Analytics,
چکیده مقاله :
For Telecom operators, customer churn, i.e., the event when the customers leave a service provider, becomes a critical concern, studies have shown that acquiring new customers cost five times more than to retain them. In competitive markets, where is increasingly important, to sustain growth as well as profitability correctly predicting the tendencies for customer churn is important. Traditional predictive fashions frequently underperform due to the complex nature of client behavior. In this examine, we introduce a unique composite deep mastering framework whose hyperparameters are optimized the usage of the Particle Swarm Optimization (PSO) set of rules. Our method integrates a couple of neural community architectures to effectively capture each spatial and temporal patterns in client interactions. The PSO set of rules systematically first-rate-tunes parameters including activation functions, regularization techniques, gaining knowledge of rates, optimizers, and neuron counts—ensuing in a model that demonstrates robust overall performance. We evaluated our approach the usage of key metrics consisting of accuracy, precision, recollect, F1 score, and ROC AUC on a numerous purchaser dataset. Comparative analyses were conducted in opposition to established deep studying fashions (LSRM_GRU, LSTM, GRU, CNN_LSTM) in addition to other conventional methods (KNN, XG_BOOST, DEEP BP-ANN, BiLSTM-CNN, and Decision Tree). Experimental results stompy that our PSO-enhanced composite deep learning model stands out significantly compared with conventional models. Comparing the ROC-AUC scores of 0.932 and 0.93, F1 scores of 0.90 and 0.895, and accuracy rates of 83.2% and 93% on both Cell2Cell and IBM Telco datasets. it is indeed effective for practical churn prediction use incitements efficiencies. Var The experimental results demonstrate that our PSO express tree model outperforms conventional methods, achieving better performance with ROC totter score above 0.932 and 0.93, F 1 scores above 0.90 and 0.895 as well as accuracy rates in excess of 83.2% (% paper) and 93% (on the Telco data set) for Cell2Cell and IBM Telco respectively. This is further confirmation of its effectiveness and promise for practical churn prediction applications.
For Telecom operators, customer churn, i.e., the event when the customers leave a service provider, becomes a critical concern, studies have shown that acquiring new customers cost five times more than to retain them. In competitive markets, where is increasingly important, to sustain growth as well as profitability correctly predicting the tendencies for customer churn is important. Traditional predictive fashions frequently underperform due to the complex nature of client behavior. In this examine, we introduce a unique composite deep mastering framework whose hyperparameters are optimized the usage of the Particle Swarm Optimization (PSO) set of rules. Our method integrates a couple of neural community architectures to effectively capture each spatial and temporal patterns in client interactions. The PSO set of rules systematically first-rate-tunes parameters including activation functions, regularization techniques, gaining knowledge of rates, optimizers, and neuron counts—ensuing in a model that demonstrates robust overall performance. We evaluated our approach the usage of key metrics consisting of accuracy, precision, recollect, F1 score, and ROC AUC on a numerous purchaser dataset. Comparative analyses were conducted in opposition to established deep studying fashions (LSRM_GRU, LSTM, GRU, CNN_LSTM) in addition to other conventional methods (KNN, XG_BOOST, DEEP BP-ANN, BiLSTM-CNN, and Decision Tree). Experimental results stompy that our PSO-enhanced composite deep learning model stands out significantly compared with conventional models. Comparing the ROC-AUC scores of 0.932 and 0.93, F1 scores of 0.90 and 0.895, and accuracy rates of 83.2% and 93% on both Cell2Cell and IBM Telco datasets. it is indeed effective for practical churn prediction use incitements efficiencies. Var The experimental results demonstrate that our PSO express tree model outperforms conventional methods, achieving better performance with ROC totter score above 0.932 and 0.93, F 1 scores above 0.90 and 0.895 as well as accuracy rates in excess of 83.2% (% paper) and 93% (on the Telco data set) for Cell2Cell and IBM Telco respectively. This is further confirmation of its effectiveness and promise for practical churn prediction 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] F. Mozaffari, I. R. Vanani, P. Mahmoudian, and B. Sohrabi, “Application of Machine Learning in the Telecommunications Industry: Partial Churn Prediction by using a Hybrid Feature Selection Approach,” Journal of Information Systems and Telecommunication, vol. 11, no. 4, 2023, doi: 10.61186/jist.38419.11.44.331.
[3] 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.
[4] 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.
[5] 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.
[6] S. A. Panimalar and A. Krishnakumar, “A review of churn prediction models using different machine learning and deep learning approaches in cloud environment,” 2023. doi: 10.14456/jcst.2023.12.
[7] L. Geiler, S. Affeldt, and M. Nadif, “A survey on machine learning methods for churn prediction,” 2022. doi: 10.1007/s41060-022-00312-5.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] M. R. Ismail, M. K. Awang, M. N. A. Rahman, and M. Makhtar, “A multi-layer perceptron approach for customer churn prediction,” International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 7, 2015, doi: 10.14257/ijmue.2015.10.7.22.
[16] S. O. Abdulsalam, J. F. Ajao, B. F. Balogun, and M. O. Arowolo, “A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms,” ICST Transactions on Mobile Communications and Applications, vol. 6, no. 21, 2022, doi: 10.4108/eetmca.v6i21.2181.
[17] I. A. Adeniran, C. P. Efunniyi, O. S. Osundare, A. O. Abhulimen, and U. OneAdvanced, “Implementing machine learning techniques for customer retention and churn prediction in telecommunications,” Computer Science & IT Research Journal, vol. 5, no. 8, 2024.
[18] M. Z. Alotaibi and M. A. Haq, “Customer churn prediction for telecommunication companies using machine learning and ensemble methods,” Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14572–14578, 2024.
[19] Y. Zhang, S. Wang, and G. Ji, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications,” 2015. doi: 10.1155/2015/931256.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] A. Jatain, S. B. Bajaj, P. Vashisht, and A. Narang, “Artificial Intelligence Based Predictive Analysis of Customer Churn,”
