Interpretable prediction of thermal sensation for elderly people based on data sampling, machine learning and SHapley Additive exPlanations (SHAP)
Authors:
- Guozhong ZhengGZGuozhong Zheng
- Yuqin ZhangYZYuqin Zhang
- Xuhui YueXYXuhui Yue
- Kang LiKLKang Li
- 8 July 2023
- journal article
- research article
- Published by Elsevier in Building and Environment
- Vol. 242
Abstract
No abstract availableKeywords
Funding Information
- Humanities and Social Science Fund of Ministry of Education of China (20YJAZH138)
This publication has 23 references indexed in Scilit:
- Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning ApproachApplied Sciences, 2019
- Land subsidence susceptibility assessment using random forest machine learning algorithmEnvironmental Earth Sciences, 2019
- Predicting older people's thermal sensation in building environment through a machine learning approach: Modelling, interpretation, and applicationBuilding and Environment, 2019
- Influence of relative air humidity and movement on human thermal perception in classrooms in a hot and humid climateBuilding and Environment, 2018
- A systematic extreme learning machine approach to analyze visitors׳ thermal comfort at a public urban spaceRenewable and Sustainable Energy Reviews, 2016
- Recommendations for using the relative operating characteristic (ROC)Landscape Ecology, 2014
- SVMs Modeling for Highly Imbalanced ClassificationIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008
- SMOTE: Synthetic Minority Over-sampling TechniqueJournal of Artificial Intelligence Research, 2002
- Random ForestsMachine Learning, 2001
- Asymptotic Properties of Nearest Neighbor Rules Using Edited DataIEEE Transactions on Systems, Man, and Cybernetics, 1972