Zhixin Kang

Work place: Dept. of Economics and Decision Sciences, University of North Carolina at Pembroke, Pembroke, U.S.A

E-mail: zhixin.kang@uncp.edu


Research Interests: Economics, Statistics


Dr. Zhixin Kang is an associate professor at the University of North Carolina at Pembroke, NC, U.S.A. His research has been published in Communications in Statistics – Theory and Methods, Journal of Real Estate Finance and Economics, Statistics and Its Interface, Journal of Real Estate Portfolio Management, Journal of Transnational Management, Applied Financial Economics, International Journal of Supply Chain and Inventory Management, International Journal of Electronic Finance, Scholarship and Practice of Undergraduate Research, and etc.

Author Articles
Using Machine Learning Algorithms to Predict First-generation College Students’ Six-year Graduation: A Case Study

By Zhixin Kang

DOI: https://doi.org/10.5815/ijitcs.2019.09.01, Pub. Date: 8 Sep. 2019

This paper studies the forecasting mechanism of the most widely used machine learning algorithms, namely linear discriminant analysis, logistic regression, k-nearest neighbors, random forests, artificial neural network, naive Bayes, classification and regression trees, support vector machines, adaptive boosting, and stacking ensemble model, in forecasting first-generation college students’ six-year graduation using the first college year’s data. Five standard evaluating metrics are used to evaluate these models. The results show that these machine learning models can significantly predict first-generation college students’ six-year graduation with mean forecasting accuracy rate spanning from 69.58% to 75.17% and median forecasting accuracy rate spanning from 70.37% to 74.52%. Among these machine learning algorithms, stacking ensemble model, logistic regression model, and linear discriminant analysis are the best three ones in terms of mean forecasting accuracy rate. Furthermore, the results from the repeated ten-fold cross-validation process reveal that the variations of the five evaluating metrics exhibit remarkably different patterns across the ten machine learning algorithms.

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