GAN data reconstruction based prediction method of telecom subscriber loss
Users are the core of operators’ interests.With the introduction of the policy of transferring network with a number, the competition between operators becomes more and more fierce.In order to accurately predict subscriber loss tendency in advance, a prediction method of subscriber loss based on gen...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-03-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023038/ |
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| _version_ | 1841530813472047104 |
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| author | Kehong A Xiaodong HU |
| author_facet | Kehong A Xiaodong HU |
| author_sort | Kehong A |
| collection | DOAJ |
| description | Users are the core of operators’ interests.With the introduction of the policy of transferring network with a number, the competition between operators becomes more and more fierce.In order to accurately predict subscriber loss tendency in advance, a prediction method of subscriber loss based on generative adversarial network data reconstruction was proposed.Firstly, the dirty data in the telecom subscriber loss data was used by effective data preprocessing method.Secondly, the GAN was used to reconstruct the telecom subscriber loss data to solve the problem of the imbalance of the telecom subscriber loss data.Finally, extreme gradient boosting algorithm was used to train the telecom subscriber loss prediction model based on GAN reconstruction and the SMOTE sampling model based on synthetic minority oversampling technique sampling method respectively, and compare the prediction accuracy of the two models.The experimental results show that the prediction accuracy of the GAN reconstructed telecom subscriber loss prediction model is increased by 6.75%, the accuracy rate is increased by 25.91%, the recall rate is increased by 30.91%, and the F1-score is increased by 28.73% compared with the unreconstructed prediction model.This method can effectively improve the accuracy of telecom subscriber loss prediction. |
| format | Article |
| id | doaj-art-30ae4088af91458ba93af4085ade7211 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-30ae4088af91458ba93af4085ade72112025-01-15T02:58:58ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-03-013913514259569937GAN data reconstruction based prediction method of telecom subscriber lossKehong AXiaodong HUUsers are the core of operators’ interests.With the introduction of the policy of transferring network with a number, the competition between operators becomes more and more fierce.In order to accurately predict subscriber loss tendency in advance, a prediction method of subscriber loss based on generative adversarial network data reconstruction was proposed.Firstly, the dirty data in the telecom subscriber loss data was used by effective data preprocessing method.Secondly, the GAN was used to reconstruct the telecom subscriber loss data to solve the problem of the imbalance of the telecom subscriber loss data.Finally, extreme gradient boosting algorithm was used to train the telecom subscriber loss prediction model based on GAN reconstruction and the SMOTE sampling model based on synthetic minority oversampling technique sampling method respectively, and compare the prediction accuracy of the two models.The experimental results show that the prediction accuracy of the GAN reconstructed telecom subscriber loss prediction model is increased by 6.75%, the accuracy rate is increased by 25.91%, the recall rate is increased by 30.91%, and the F1-score is increased by 28.73% compared with the unreconstructed prediction model.This method can effectively improve the accuracy of telecom subscriber loss prediction.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023038/XGBoost algorithmgenerative adversarial networkcustomer churndata reconstructionsynthetic mi-nority oversampling technique |
| spellingShingle | Kehong A Xiaodong HU GAN data reconstruction based prediction method of telecom subscriber loss Dianxin kexue XGBoost algorithm generative adversarial network customer churn data reconstruction synthetic mi-nority oversampling technique |
| title | GAN data reconstruction based prediction method of telecom subscriber loss |
| title_full | GAN data reconstruction based prediction method of telecom subscriber loss |
| title_fullStr | GAN data reconstruction based prediction method of telecom subscriber loss |
| title_full_unstemmed | GAN data reconstruction based prediction method of telecom subscriber loss |
| title_short | GAN data reconstruction based prediction method of telecom subscriber loss |
| title_sort | gan data reconstruction based prediction method of telecom subscriber loss |
| topic | XGBoost algorithm generative adversarial network customer churn data reconstruction synthetic mi-nority oversampling technique |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023038/ |
| work_keys_str_mv | AT kehonga gandatareconstructionbasedpredictionmethodoftelecomsubscriberloss AT xiaodonghu gandatareconstructionbasedpredictionmethodoftelecomsubscriberloss |