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.