Predictive Models for Imbalanced Data: A School Dropout Perspective

Predictive Models for Imbalanced Data: A School Dropout Perspective

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to p...

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Bibliographic Details
Main Authors: Thiago M. Barros, Plácido A. Souza Neto, Ivanovitch Silva, Luiz Affonso Guedes
Format: Article
Language:English
Published: MDPI AG 2019-11-01
Series:Education Sciences
Subjects:
dropout rates
accuracy paradox
imbalanced learning
downsample
g-mean predict
mlp
decision tree
balanced bagging
uar
smote
adasyn
Online Access:https://www.mdpi.com/2227-7102/9/4/275

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https://www.mdpi.com/2227-7102/9/4/275

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