research-article

Customer Churn Combination Prediction Model Based on Convolutional Neural Network and Gradient Boosting Decision Tree

Authors:

Xianquan ZhangAuthors Info & Claims

ACAI '22: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 12, Pages 1 - 6

https://doi.org/10.1145/3579654.3579666

Published: 14 March 2023 Publication History

Abstract

In order to improve the hit ratio of lost customers in telecom industry, a combination prediction model of customer churn based on one-dimensional convolutional neural network(1DCNN) and gradient boosting decision tree(GBDT) is proposed. Firstly, customer data is fed into 1DCNN model, which uses one-dimensional convolution to automatically extract customer features and then predicts customer churn through full connection layer. If the prediction result of 1DCNN model is churn, the result is directly output. If the prediction result is non-churn, the customer data will be re-introduced into GBDT model for second forecast, and the new prediction result will be output. Experiments on two publicly available telecom customer data set show that the proposed combined model significantly improves the recall rate and F1 score of customer churn prediction.

References

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Cited By

Johnson OXinYing CJohnson OKhaw KLee M(2024)Learning Rate Schedules and Optimizers, A Game Changer for Deep Neural NetworksAdvances in Intelligent Computing Techniques and Applications10.1007/978-3-031-59711-4_28(327-340)Online publication date: 30-Jun-2024
https://doi.org/10.1007/978-3-031-59711-4_28
Johnson OXinying CKhaw KLee M(2023)ps-CALR: Periodic-Shift Cosine Annealing Learning Rate for Deep Neural NetworksIEEE Access10.1109/ACCESS.2023.334071911(139171-139186)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3340719

Index Terms

Customer Churn Combination Prediction Model Based on Convolutional Neural Network and Gradient Boosting Decision Tree
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

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ACAI '22: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

December 2022

770 pages

ISBN:9781450398336

DOI:10.1145/3579654

Copyright © 2022 ACM.

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Published: 14 March 2023

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ACAI 2022

ACAI 2022: 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

December 23 - 25, 2022

Sanya, China

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Cited By

Johnson OXinYing CJohnson OKhaw KLee M(2024)Learning Rate Schedules and Optimizers, A Game Changer for Deep Neural NetworksAdvances in Intelligent Computing Techniques and Applications10.1007/978-3-031-59711-4_28(327-340)Online publication date: 30-Jun-2024
https://doi.org/10.1007/978-3-031-59711-4_28
Johnson OXinying CKhaw KLee M(2023)ps-CALR: Periodic-Shift Cosine Annealing Learning Rate for Deep Neural NetworksIEEE Access10.1109/ACCESS.2023.334071911(139171-139186)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3340719

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