Abstract
In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analytics in higher education. The proposed experiment is based on a combination of classic machine learning algorithms such as Naive Bayes and Random Forest with various ensemble methods such as Stochastic, Linear Discriminant Analysis (LDA), Tree model (C5.0), Bagged CART (treebag) and K Nearest Neighbors (KNN). We applied traditional classification methods to classify the students’ performance and to determine the independent variables that offer the highest accuracy. Our results depict that the data with the 11 features using random forest generated the best accuracy value of 0.7333. However, we revised the experiment with ensemble algorithms to reduce the variance (bagging), bias (boosting) and to improve the prediction accuracy (stacking). Consequently, the bagging random forest outperformed other methods with the accuracy value of 0.7959.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Brohi, S.N., Pillai, T.R., Kaur, S., Kaur, H., Sukumaran, S., Asirvatham, D. (2019). Accuracy Comparison of Machine Learning Algorithms for Predictive Analytics in Higher Education. In: Miraz, M., Excell, P., Ware, A., Soomro, S., Ali, M. (eds) Emerging Technologies in Computing. iCETiC 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-23943-5_19
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