{"title":"基于XGBoost的可解释学习光网络故障预测算法","authors":"Chunyu Zhang, Danshi Wang, Chuang Song, Lingling Wang, Jianan Song, Luyao Guan, Min Zhang","doi":"10.1364/ofc.2020.th1f.3","DOIUrl":null,"url":null,"abstract":"We propose a fault prediction scheme using interpretable XGBoost based on actual datasets, which not only achieves high accuracy (99.72%) and low positive rate (0.18%), but also reveals the five most remarkable features that caused the fault.","PeriodicalId":173355,"journal":{"name":"2020 Optical Fiber Communications Conference and Exhibition (OFC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Interpretable Learning Algorithm Based on XGBoost for Fault Prediction in Optical Network\",\"authors\":\"Chunyu Zhang, Danshi Wang, Chuang Song, Lingling Wang, Jianan Song, Luyao Guan, Min Zhang\",\"doi\":\"10.1364/ofc.2020.th1f.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a fault prediction scheme using interpretable XGBoost based on actual datasets, which not only achieves high accuracy (99.72%) and low positive rate (0.18%), but also reveals the five most remarkable features that caused the fault.\",\"PeriodicalId\":173355,\"journal\":{\"name\":\"2020 Optical Fiber Communications Conference and Exhibition (OFC)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Optical Fiber Communications Conference and Exhibition (OFC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/ofc.2020.th1f.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Optical Fiber Communications Conference and Exhibition (OFC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/ofc.2020.th1f.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpretable Learning Algorithm Based on XGBoost for Fault Prediction in Optical Network
We propose a fault prediction scheme using interpretable XGBoost based on actual datasets, which not only achieves high accuracy (99.72%) and low positive rate (0.18%), but also reveals the five most remarkable features that caused the fault.
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