Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class imbalance in the dataset. This study has aimed to (i)...

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Bibliographic Details
Main Authors: Theng-Jia Law, Choo-Yee Ting, Hu Ng, Hui-Ngo Goh, Albert Quek
Format: Article
Language:English
Published: MMU Press 2024-06-01
Series:Journal of Informatics and Web Engineering
Subjects:
graduate on time
in-university
class imbalance
artificial intelligence
machine learning
Online Access:https://journals.mmupress.com/index.php/jiwe/article/view/1076
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