Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model

被引:9
作者
Li J. [1 ]
Sawaragi T. [1 ]
Horiguchi Y. [2 ]
机构
[1] Department of Mechanical Engineering and Science, Kyoto University, Kyoto
[2] Faculty of Informatics, Kansai University, Osaka
关键词
causal analysis; explainable model; healthcare; Machine learning; structural equation modelling;
D O I
10.1080/18824889.2021.1894040
中图分类号
学科分类号
摘要
With the development of artificial intelligence technologies, the high accuracy of machine learning methods has become a non-unique standard. People are beginning to be more concerned about the understandability between humans and machines. The interference procedure of the machines is hoped to accord with human thinking as much as possible, which has spawned the recent and ongoing demands for developing explainable models. The present study proposes a new explainable and persuasive model for machine learning problems by introducing Structural Equation Modelling into the picture. Six parts make up the model, from data collection to model evaluation. The model can be used for data analysis, machine learning, and causal analysis. The proposed model is also transparent and can be interpreted from design to application. A practical experiment shows its effectiveness in a healthcare problem. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:67 / 79
页数:12
相关论文
共 50 条
[21]   A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning [J].
Kneifl, Jonas ;
Grunert, Dennis ;
Fehr, Joerg .
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2021, 122 (17) :4774-4786
[22]   A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context [J].
Lozano-Murcia, Catalina ;
Romero, Francisco P. ;
Serrano-Guerrero, Jesus ;
Olivas, Jose A. .
MATHEMATICS, 2023, 11 (14)
[23]   Solution of structural mechanic's problems by machine learning [J].
Gaur, Himanshu ;
Khidhir, Basim ;
Manchiryal, Ram Kishore .
INTERNATIONAL JOURNAL OF HYDROMECHATRONICS, 2022, 5 (01) :22-43
[24]   Online learning: a stakeholders' analysis using structural equation modelling [J].
Panwar, Manoj ;
Garg, Ramesh Kumar .
INTERNATIONAL JOURNAL OF TECHNOLOGY ENHANCED LEARNING, 2022, 14 (03) :243-263
[25]   Integrating explainable machine learning and user-centric model for diagnosing cardiovascular disease: A novel approach [J].
Dharmarathne, Gangani ;
Bogahawaththa, Madhusha ;
Rathnayake, Upaka ;
Meddage, D. P. P. .
INTELLIGENT SYSTEMS WITH APPLICATIONS, 2024, 23
[26]   Ensemble machine learning framework for daylight modelling of various building layouts [J].
Alsharif, Rashed ;
Arashpour, Mehrdad ;
Golafshani, Emad ;
Bazli, Milad ;
Mohandes, Saeed Reza .
BUILDING SIMULATION, 2023, 16 (11) :2049-2061
[27]   Ensemble machine learning framework for daylight modelling of various building layouts [J].
Rashed Alsharif ;
Mehrdad Arashpour ;
Emad Golafshani ;
Milad Bazli ;
Saeed Reza Mohandes .
Building Simulation, 2023, 16 :2049-2061
[28]   Dissecting the Predictors of Cyber-Aggression Through an Explainable Machine Learning Model [J].
Zhu, Wenfeng ;
Wang, Kai ;
Liu, Songyu ;
Sha, Qianli ;
Yang, Yuguang ;
Wang, Qiang ;
Tian, Xue .
AGGRESSIVE BEHAVIOR, 2025, 51 (01)
[29]   Explainable Machine Learning Model for Predicting Drift Capacity of Reinforced Concrete Walls [J].
Aladsani, Muneera A. ;
Burton, Henry ;
Abdullah, Saman A. ;
Wallace, John W. .
ACI STRUCTURAL JOURNAL, 2022, 119 (03) :191-204
[30]   Building a predictive machine learning model of gentrification in Sydney [J].
Thackway, William ;
Ng, Matthew ;
Lee, Chyi-Lin ;
Pettit, Christopher .
CITIES, 2023, 134