{"title":"基于XGboost的网络业务类型分类方法","authors":"Tiantian Lv, Yanqin Wu, Le Zhang","doi":"10.1109/CCISP55629.2022.9974197","DOIUrl":null,"url":null,"abstract":"In order to distinguish different types of network services and analyze the needs of customers of different types of services to ensure user perception. This paper proposes a classification method of network business type based on XGboost. Firstly, a feature contribution analysis method based on Pearson correlation coefficient is proposed, then the classification model of network business type based on XGboost is constructed to classify network business type. Finally, RMSE is used to evaluation the classification method. Compared with other models, the RMSE of XGboost model is no more than 3.5, lower than LSTM, ARIMA, LR and SVM model, which proves the reliability of the XGboost model in network business type classification.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A classification method of network business type based on XGboost\",\"authors\":\"Tiantian Lv, Yanqin Wu, Le Zhang\",\"doi\":\"10.1109/CCISP55629.2022.9974197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to distinguish different types of network services and analyze the needs of customers of different types of services to ensure user perception. This paper proposes a classification method of network business type based on XGboost. Firstly, a feature contribution analysis method based on Pearson correlation coefficient is proposed, then the classification model of network business type based on XGboost is constructed to classify network business type. Finally, RMSE is used to evaluation the classification method. Compared with other models, the RMSE of XGboost model is no more than 3.5, lower than LSTM, ARIMA, LR and SVM model, which proves the reliability of the XGboost model in network business type classification.\",\"PeriodicalId\":431851,\"journal\":{\"name\":\"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCISP55629.2022.9974197\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCISP55629.2022.9974197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A classification method of network business type based on XGboost
In order to distinguish different types of network services and analyze the needs of customers of different types of services to ensure user perception. This paper proposes a classification method of network business type based on XGboost. Firstly, a feature contribution analysis method based on Pearson correlation coefficient is proposed, then the classification model of network business type based on XGboost is constructed to classify network business type. Finally, RMSE is used to evaluation the classification method. Compared with other models, the RMSE of XGboost model is no more than 3.5, lower than LSTM, ARIMA, LR and SVM model, which proves the reliability of the XGboost model in network business type classification.