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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/7/2/83 |
| _version_ | 1797596118162341888 |
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| author | Khrystyna Zub Pavlo Zhezhnych Christine Strauss |
| author_facet | Khrystyna Zub Pavlo Zhezhnych Christine Strauss |
| author_sort | Khrystyna Zub |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:47:02Z |
| format | Article |
| id | doaj.art-aece5b3385fd4efba2362731db552f78 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-11T02:47:02Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-aece5b3385fd4efba2362731db552f782023-11-18T09:18:31ZengMDPI AGBig Data and Cognitive Computing2504-22892023-04-01728310.3390/bdcc7020083Two-Stage PNN–SVM Ensemble for Higher Education Admission PredictionKhrystyna Zub0Pavlo Zhezhnych1Christine Strauss2Department of Social Communication and Information Activities, Lviv Polytechnic National University, 79000 Lviv, UkraineDepartment of Social Communication and Information Activities, Lviv Polytechnic National University, 79000 Lviv, UkraineFaculty of Business, Economics and Statistics, University of Vienna, 1090 Vienna, AustriaIn 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.https://www.mdpi.com/2504-2289/7/2/83higher education institutionadmission predictionsupport vector machineensemble stackinglinear regressionprobabilistic neural network |
| spellingShingle | Khrystyna Zub Pavlo Zhezhnych Christine Strauss Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction Big Data and Cognitive Computing higher education institution admission prediction support vector machine ensemble stacking linear regression probabilistic neural network |
| title | Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction |
| title_full | Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction |
| title_fullStr | Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction |
| title_full_unstemmed | Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction |
| title_short | Two-Stage PNN–SVM Ensemble for Higher Education Admission Prediction |
| title_sort | two stage pnn svm ensemble for higher education admission prediction |
| topic | higher education institution admission prediction support vector machine ensemble stacking linear regression probabilistic neural network |
| url | https://www.mdpi.com/2504-2289/7/2/83 |
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