Application of Stacking Ensemble Machine in Big Data: Analyze the Determinants for Vitalization of the Multicultural Support Center

Abstract

For multicultural families to successfully promote social adaptation and achieve desirable social integration, the role of the multicultural family support center (Multi-FSC) is crucial. In addition, it's important to examine the factors that will contribute to the multicultural support center's vitality from the standpoint of the customers. In this study, machine learning models based on a single machine learning model and stacking ensemble using survey data from all multicultural families were used to examine the determinants for the utilization of multicultural family support centers for multicultural families. Additionally, based on the constructed prediction model, this study offered the foundational data for the revitalization of the multicultural support center. In this study, 281,606 adults (19 years or older), 56,273 of whom were married immigrants or naturalized citizens as of 2012, were examined. The stacking ensemble method was employed in this work to forecast the use of multicultural family support centers. In the base stage (model) of this model, logistic regression was employed, along with Classification and Regression Tree (CART), Radial Basis Function Neural Network (RBF-NN), and Random Forest (RF) model. The RBF-NN-Logit reg model had the best prediction performance, according to the study's findings (RMSE = 0.20, Ev = 0.45, and IA = 0.68). The findings of this study suggested that the prediction performance of the stacking ensemble can be improved when creating classification or prediction models using epidemiological data from a community.