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 individual prediction models and stacking ensemble models. This study analyzed 19,431 adolescents (between 19 and 24 years old: 9,835 males and 9,596 females) among the children of marriage immigrants. This study used random forest (RF), rotation forest, artificial neural network (ANN), and support vector machine (SVM) for the base model. Logistic regression algorithm was applied for the meta model. Each machine learning model was built through 5-fold cross-validation. Root-mean-square-error (RMSE), index of agreement (IA), and variance of errors (Ev) were used to evaluate the prediction performance of the developed models. The results of this study indicated that the prediction performance of the rotation forest-logistic regression model had the best performance. The future studies need to explore stacking ensemble models with the best performance through combining a base model and a meta model by using various machine learning algorithms such as clustering and boosting.

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study

“…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.…”

“…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.…”

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

“…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.…”

Development of a Stacking-Based Ensemble Machine Learning for Detection of Depression in Parkinson’s Disease: Preliminary Research

“…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].…”

“…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.…”