RFA-XGBoost模型在移动网络潜在投诉用户预测中的应用

张鹏; 高源

doi:10.11959/j.issn.1000-0801.2025036

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RFA-XGBoost模型在移动网络潜在投诉用户预测中的应用

工程与应用 | 更新时间：2025-04-24

- RFA-XGBoost模型在移动网络潜在投诉用户预测中的应用
- Application of the RFA-XGBoost model in predicting potential complaint users in mobile network
- 电信科学 2025年41卷第3期页码：167-178
- 作者机构：
  
  中国移动通信集团设计院有限公司湖北分公司，湖北武汉 430021
- 作者简介：
  
  [ "张鹏（1980- ），女，中国移动通信集团设计院有限公司湖北分公司工程师，主要研究方向为网络研究、咨询及网络智能化。" ]
  [ "高源（1986- ），男，中国移动通信集团设计院有限公司湖北分公司工程师，主要研究方向为IT信息化。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025036
  中图分类号： TP391
- 收稿：2024-11-08，
  
  修回：2025-02-11，
  
  纸质出版：2025-03-20
- 稿件说明：
移动端阅览
张鹏,高源.RFA-XGBoost模型在移动网络潜在投诉用户预测中的应用[J].电信科学,2025,41(03):167-178.

ZHANG Peng,GAO Yuan.Application of the RFA-XGBoost model in predicting potential complaint users in mobile network[J].Telecommunications Science,2025,41(03):167-178.
张鹏,高源.RFA-XGBoost模型在移动网络潜在投诉用户预测中的应用[J].电信科学,2025,41(03):167-178. DOI： 10.11959/j.issn.1000-0801.2025036.

ZHANG Peng,GAO Yuan.Application of the RFA-XGBoost model in predicting potential complaint users in mobile network[J].Telecommunications Science,2025,41(03):167-178. DOI： 10.11959/j.issn.1000-0801.2025036.

摘要

为了提前预测并减少移动网络用户投诉事件的发生，深入研究了多维数据分析在移动网络潜在投诉用户预测中的应用。采集移动网络用户广泛的业务域和运营域指标作为输入特征数据，成功构建了基于极限梯度提升（extreme gradient boosting，XGBoost）算法的潜在投诉用户预测模型，该模型在测试集上平均预测准确率达96.35%。同时，提出了迭代特征增强XGBoost（recursive feature augmented XGBoost，RFA-XGBoost）预测模型用于潜在投诉用户预测，即通过不断迭代将前一轮XGBoost模型的预测输出作为新的特征添加到特征集中，并重新训练新一轮的XGBoost模型，优化后的平均预测准确率可提升至98.89%。所提研究成果对于移动网络运营商而言，意味着能够更早地识别并介入潜在投诉情况，从而有效预防投诉事件的发生，进一步提升用户满意度和服务质量，具有重要的实践意义和商业价值。

Abstract

In order to predict and reduce the occurrence of complaints of mobile network users in advance

the application of multidimensional data analysis in the prediction of potential complaints of mobile network users was deeply studied. By collecting a wide range of business domain and operation domain indicators of mobile network users as input feature data

a potential complaint user prediction model based on extreme gradient boosting (XGBoost) was successfully constructed

which had an average prediction accuracy of 96.35% on the test set. At the same time

the recursive feature augmented XGBoost (RFA-XGBoost) prediction model was proposed for the prediction of potential complaint users. By iteratively adding the predicted output of the previous round of XGBoost model to the feature set as a new feature and retraining the new round of XGBoost model

the average prediction accuracy after optimization could be improved to 98.89%. For mobile network operators

the research results mean that they can identify and intervene in potential complaints earlier

so as to effectively prevent the occurrence of complaints and further improve user satisfaction and service quality

which has important practical significance and commercial value.

关键词

Keywords

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