Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database

Subjective Well-Being (SWB) is an important indicator reflecting the satisfaction of residents’ lives and social welfare. As a prevalent technique, machine learning is playing a more significant role in various domains. However, few studies have used machine learning techniques to study SWB. This pa...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Na Ke, Guoqing Shi, Ying Zhou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	subjective well-being stacking model machine learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/a902fd1393904ae0ba2db8f5deed1499
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a902fd1393904ae0ba2db8f5deed1499
record_format	dspace
spelling	oai:doaj.org-article:a902fd1393904ae0ba2db8f5deed14992021-11-11T19:32:02ZStacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database10.3390/su1321118332071-1050https://doaj.org/article/a902fd1393904ae0ba2db8f5deed14992021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11833https://doaj.org/toc/2071-1050Subjective Well-Being (SWB) is an important indicator reflecting the satisfaction of residents’ lives and social welfare. As a prevalent technique, machine learning is playing a more significant role in various domains. However, few studies have used machine learning techniques to study SWB. This paper puts forward a stacking model based on ANN, XGBoost, LR, CatBoost, and LightGBM to predict the SWB of Chinese residents, using the Chinese General Social Survey (CGSS) datasets from 2011, 2013, 2015, and 2017. Furthermore, the feature importance index of tree models is used to reveal the changes in the important factors affecting SWB. The results show that the stacking model proposed in this paper is superior to traditional models such as LR or other single machine learning models. The results also show some common features that have contributed to SWB in different years. The methods used in this study are effective and the results provide support for making society more harmonious.Na KeGuoqing ShiYing ZhouMDPI AGarticlesubjective well-beingstacking modelmachine learningEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11833, p 11833 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	subjective well-being stacking model machine learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	subjective well-being stacking model machine learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Na Ke Guoqing Shi Ying Zhou Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database
description	Subjective Well-Being (SWB) is an important indicator reflecting the satisfaction of residents’ lives and social welfare. As a prevalent technique, machine learning is playing a more significant role in various domains. However, few studies have used machine learning techniques to study SWB. This paper puts forward a stacking model based on ANN, XGBoost, LR, CatBoost, and LightGBM to predict the SWB of Chinese residents, using the Chinese General Social Survey (CGSS) datasets from 2011, 2013, 2015, and 2017. Furthermore, the feature importance index of tree models is used to reveal the changes in the important factors affecting SWB. The results show that the stacking model proposed in this paper is superior to traditional models such as LR or other single machine learning models. The results also show some common features that have contributed to SWB in different years. The methods used in this study are effective and the results provide support for making society more harmonious.
format	article
author	Na Ke Guoqing Shi Ying Zhou
author_facet	Na Ke Guoqing Shi Ying Zhou
author_sort	Na Ke
title	Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database
title_short	Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database
title_full	Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database
title_fullStr	Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database
title_full_unstemmed	Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database
title_sort	stacking model for optimizing subjective well-being predictions based on the cgss database
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a902fd1393904ae0ba2db8f5deed1499
work_keys_str_mv	AT nake stackingmodelforoptimizingsubjectivewellbeingpredictionsbasedonthecgssdatabase AT guoqingshi stackingmodelforoptimizingsubjectivewellbeingpredictionsbasedonthecgssdatabase AT yingzhou stackingmodelforoptimizingsubjectivewellbeingpredictionsbasedonthecgssdatabase
_version_	1718431485838491648

Stacking Model for Optimizing Subjective Well-Being Predictions Based on the CGSS Database

Ejemplares similares