{"title":"使用无监督方法插值和外推机器学习模型","authors":"Peng Liu","doi":"10.36001/phmconf.2023.v15i1.3794","DOIUrl":null,"url":null,"abstract":"The 2023 PHM North America Data Challenge is intriguing because it requires one to predict outcomes and use data patterns that training models do not see. Modern machine learning models based on gradient boosting and neural networks are not designed to address such issues in usually circumstances. Our final approach to address the challenge consists of five steps. In our approach, we use an unsupervised method besides machine learning models to address the challenge.","PeriodicalId":91951,"journal":{"name":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","volume":"69 7","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Interpolate and Extrapolate Machine Learning Models using An Unsupervised Method\",\"authors\":\"Peng Liu\",\"doi\":\"10.36001/phmconf.2023.v15i1.3794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The 2023 PHM North America Data Challenge is intriguing because it requires one to predict outcomes and use data patterns that training models do not see. Modern machine learning models based on gradient boosting and neural networks are not designed to address such issues in usually circumstances. Our final approach to address the challenge consists of five steps. In our approach, we use an unsupervised method besides machine learning models to address the challenge.\",\"PeriodicalId\":91951,\"journal\":{\"name\":\"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference\",\"volume\":\"69 7\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36001/phmconf.2023.v15i1.3794\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36001/phmconf.2023.v15i1.3794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpolate and Extrapolate Machine Learning Models using An Unsupervised Method
The 2023 PHM North America Data Challenge is intriguing because it requires one to predict outcomes and use data patterns that training models do not see. Modern machine learning models based on gradient boosting and neural networks are not designed to address such issues in usually circumstances. Our final approach to address the challenge consists of five steps. In our approach, we use an unsupervised method besides machine learning models to address the challenge.