2021
DOI: 10.14569/ijacsa.2021.0120516
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Exploring Factors Associated with the Social Discrimination Experience of Children from Multicultural Families in South Korea by using Stacking with Non-linear Algorithm

Abstract: The number of children from multicultural families is increasing rapidly along with quickly increasing multicultural families. However, there are not enough surveys and basic researches for understanding the characteristics of multicultural children and issues such as social discrimination. This study discovered the machine learning model with the best performance for predicting the social discrimination experience of children from multicultural families by comparing the prediction performance (accuracy) of in… Show more

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Cited by 4 publications
(7 citation statements)
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References 24 publications
(17 reference statements)
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“…SVM + RF + LGBM + AdaBoost + XGBoost, LightGBM + XGBoost, and Adaboost + XGBoost, among the stacking ensemble models in this study, had higher accuracy, precision, recall, and F1-score than single predictive models. The results agreed with previous studies [42,43], which reported that the root-mean-square error (RMSE) of the stacking ensemble model was lower than that of the single machine learning model. In particular, Byeon (2021) [43] showed that the stacking ensemble model had a higher index of agreement (IA) and variance of errors (Ev), in addition to accuracy, than the single machine learning model, which implied that the predictive performance of the stacking ensemble model could be higher than that of the single predictive model for structured data such as examination data.…”
Section: Discussionsupporting
confidence: 92%
See 1 more Smart Citation
“…SVM + RF + LGBM + AdaBoost + XGBoost, LightGBM + XGBoost, and Adaboost + XGBoost, among the stacking ensemble models in this study, had higher accuracy, precision, recall, and F1-score than single predictive models. The results agreed with previous studies [42,43], which reported that the root-mean-square error (RMSE) of the stacking ensemble model was lower than that of the single machine learning model. In particular, Byeon (2021) [43] showed that the stacking ensemble model had a higher index of agreement (IA) and variance of errors (Ev), in addition to accuracy, than the single machine learning model, which implied that the predictive performance of the stacking ensemble model could be higher than that of the single predictive model for structured data such as examination data.…”
Section: Discussionsupporting
confidence: 92%
“…The results agreed with previous studies [42,43], which reported that the root-mean-square error (RMSE) of the stacking ensemble model was lower than that of the single machine learning model. In particular, Byeon (2021) [43] showed that the stacking ensemble model had a higher index of agreement (IA) and variance of errors (Ev), in addition to accuracy, than the single machine learning model, which implied that the predictive performance of the stacking ensemble model could be higher than that of the single predictive model for structured data such as examination data. However, in this study, the F1-score of SVM was 0.5% higher than that of SVM + XGBoost, which suggested that the stacking ensemble model could perform worse than a single machine learning model depending on the combination of a base model and a meta model.…”
Section: Discussionsupporting
confidence: 92%
“…The regression algorithm increases the reliability of the base model while maximizing the stability of the model (25). Previous studies (25,26) also reported that the predictive performance such as accuracy was improved compared to a single predictive model, when regression was used for the meta model. Therefore, this study used the regression algorithm for the meta model.…”
Section: Meta Modelmentioning
confidence: 99%
“…Recently, machine learning such as SVM and random forest has been used as a method to identify predictors of Parkinson's disease [8][9][10]. Among them, the stacking ensemble machine, which improves the accuracy by combining two or more single machine learning with a meta-model, can reduce the risk of bias that a single machine learning model can have [11]. Moreover, it has been confirmed that its accuracy is higher in predicting outcome variables [11].…”
Section: Introductionmentioning
confidence: 99%
“…Among them, the stacking ensemble machine, which improves the accuracy by combining two or more single machine learning with a meta-model, can reduce the risk of bias that a single machine learning model can have [11]. Moreover, it has been confirmed that its accuracy is higher in predicting outcome variables [11]. It uses a stacking ensemble machine algorithm to obtain better prediction performance than the performance obtained from a single algorithm.…”
Section: Introductionmentioning
confidence: 99%