Two-Stage PNN-SVM Ensemble for Higher Education Admission Prediction

In this paper, we investigate the methods used to evaluate the admission chances of higher education institutions' (HEI) entrants as a crucial factor that directly influences the admission efficiency, quality of education results, and future students' life-long trajectories. Due to the conditions of uncertainty surrounding the decision-making process that determines the admission of entrants and the inability to independently assess the probability of potential outcomes, we propose the application of the machine learning (ML) model as an algorithm that provides decision-making support. The proposed model includes the support vector machine (SVM) stacking ensemble, which expands the input data set obtained using the Probabilistic Neural Network (PNN). The basic algorithms include four SVM ensemble methods with different kernel functions and Logistic Regression (LR) as a meta-algorithm. We evaluate the accuracy of the developed model in three stages: comparison with existing ML methods; comparison with a single-based model that comprises it; and comparison with a similar stacking model and with other types of ensembles (boosting, begging). The results of the designed two-stage PNN-SVM ensemble model provided an accuracy of 94% and possessed acquired superiority in the comparison stages. The obtained results enable the use of the presented model in the subsequent stages of the development of an intellectual support system for decision making regarding entrants' admission.