Abstract

The classification of educational data provides valuable information for understanding student performance trends. An ensemble is a method in machine learning that uses the strength of multiple models to achieve better accuracy and performance than individual models. Combining classifiers is one of the approaches in ensemble methods, where multiple classifier models are combined to improve overall predictive performance. The combination of multiple classifiers (MCS) provides more useful insights into student performance classification than single base learners, and the outcome is determined through a majority voting (MV) technique. However, unlike equal weighting in MV, weighted MV assigns weights to base classifiers to enhance classification performance based on their confidence. This study introduces multiple classifiers using weighted majority voting (MCS-WeMV), which evaluates the individual performance of each classifier within the ensemble and adjusts their weights based on classifier confidence to contribute to the final class prediction. During experimental investigations, the weight for each classifier within a collection of classification algorithms such as decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and naive Bayes (NB) was computed. The findings indicate that the proposed MCS-WeMV outperformed the other three ensemble algorithms, AdaBoost, Bagging, and Stacking, by obtaining an accuracy of 88.15% through the combination of RF, SVM, and DT classifiers.

Keywords

Ensemble method, Multiple classifiers, Majority voting, Weighted majority voting, Classifier fusions, and Combination rule.

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