Interpolate and Extrapolate Machine Learning Models using An Unsupervised Method

Peng Liu
{"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}
引用次数: 1

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.
使用无监督方法插值和外推机器学习模型
2023年PHM北美数据挑战赛很有趣,因为它需要预测结果,并使用训练模型看不到的数据模式。在通常情况下,基于梯度增强和神经网络的现代机器学习模型并不是为了解决这些问题而设计的。我们解决这一挑战的最后方法包括五个步骤。在我们的方法中,除了机器学习模型之外,我们还使用了一种无监督的方法来解决这一挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信