Estimation of Prediction Intervals in Neural Network-Based Regression Models

Kristian Miok
{"title":"Estimation of Prediction Intervals in Neural Network-Based Regression Models","authors":"Kristian Miok","doi":"10.1109/SYNASC.2018.00078","DOIUrl":null,"url":null,"abstract":"Currently there are various methods allowing the construction of predictive models based on data. Measuring prediction uncertainty plays an essential role in fields such as medicine, physics and biology where the information about prediction accuracy can be essential. In this context only a few approaches address the question of how much the predicted values can be trusted. Neural networks are popular models, but unlike the statistical models, they do not quantify the uncertainty involved in the prediction process. In this work we investigate several regression models with a focus on estimating prediction intervals that statistical and machine learning models can provide. The analysis is conducted for a case study aiming to predict the number of crayfish in Romanian rivers based on landscape and water quality information.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2018.00078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

Currently there are various methods allowing the construction of predictive models based on data. Measuring prediction uncertainty plays an essential role in fields such as medicine, physics and biology where the information about prediction accuracy can be essential. In this context only a few approaches address the question of how much the predicted values can be trusted. Neural networks are popular models, but unlike the statistical models, they do not quantify the uncertainty involved in the prediction process. In this work we investigate several regression models with a focus on estimating prediction intervals that statistical and machine learning models can provide. The analysis is conducted for a case study aiming to predict the number of crayfish in Romanian rivers based on landscape and water quality information.
基于神经网络的回归模型预测区间的估计
目前有多种方法允许基于数据构建预测模型。测量预测不确定性在医学、物理学和生物学等领域起着至关重要的作用,在这些领域中,有关预测准确性的信息可能是必不可少的。在这种情况下,只有少数方法解决了预测值可以信任多少的问题。神经网络是流行的模型,但与统计模型不同,它们不能量化预测过程中涉及的不确定性。在这项工作中,我们研究了几种回归模型,重点是估计统计和机器学习模型可以提供的预测区间。该分析是为一个案例研究进行的,旨在根据景观和水质信息预测罗马尼亚河流中小龙虾的数量。
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:481959085
Book学术官方微信