A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining

A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining

Educational data mining is capable of producing useful data-driven applications (e.g., early warning systems in schools or the prediction of students’ academic achievement) based on predictive models. However, the class imbalance problem in educational datasets could hamper the accuracy of predictiv...

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Bibliographic Details
Main Authors: Tarid Wongvorachan, Surina He, Okan Bulut
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Information
Subjects:
class imbalance
educational data mining
oversampling
undersampling
SMOTE
SMOTE-NC
Online Access:https://www.mdpi.com/2078-2489/14/1/54

Internet

https://www.mdpi.com/2078-2489/14/1/54

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