Improve Imbalanced Multiclass Classification Based on Modified SMOTE and Feature Selection for Student Grade Prediction

Abstract

In higher education institutions (HEI), the ability to predict student grades as an early warning system is one of the important areas that gained attention to improve educational outcomes. Over the years, machine learning techniques have facilitated and successfully addressed student grade prediction for identifying the potentially weak students in a particular course. However, dealing with an imbalanced multiclass classification dataset is challenging due to biased results towards predicting the minority class. Therefore, this chapter proposes a method that can increase the classification performance by using a modified synthetic minority oversampling technique and feature selection (MSMOTE-FS). The experiments tested the proposed method's effectiveness by utilizing four oversampling techniques and six standard classification algorithms. This finding indicated that the proposed method gives promising results to improve the accuracy in multiclass classification of student grade prediction.