Version 1
: Received: 18 March 2024 / Approved: 18 March 2024 / Online: 18 March 2024 (13:08:31 CET)
How to cite:
Sedighimanesh, M.; Sedighimanesh, A.; Gheisari, M. Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques. Preprints2024, 2024031048. https://doi.org/10.20944/preprints202403.1048.v1
Sedighimanesh, M.; Sedighimanesh, A.; Gheisari, M. Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques. Preprints 2024, 2024031048. https://doi.org/10.20944/preprints202403.1048.v1
Sedighimanesh, M.; Sedighimanesh, A.; Gheisari, M. Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques. Preprints2024, 2024031048. https://doi.org/10.20944/preprints202403.1048.v1
APA Style
Sedighimanesh, M., Sedighimanesh, A., & Gheisari, M. (2024). Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques. Preprints. https://doi.org/10.20944/preprints202403.1048.v1
Chicago/Turabian Style
Sedighimanesh, M., Ali Sedighimanesh and Mehdi Gheisari. 2024 "Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques" Preprints. https://doi.org/10.20944/preprints202403.1048.v1
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, 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. Future research should explore the scalability of this approach across various sectors and further refine predictive accuracy with evolving customer data.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
