OUCH: Oversampling and Undersampling Cannot Help Improve Accuracy in Our Bayesian Classifiers That Predict Preeclampsia

Unbalanced data can have an impact on the machine learning (ML) algorithms that build predictive models. This manuscript studies the influence of oversampling and undersampling strategies on the learning of the Bayesian classification models that predict the risk of suffering preeclampsia. Given the...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Mathematics
المؤلفون الرئيسيون: Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elena Tolozano-Benitez, Víctor Gómez-Rodríguez, Lorenzo Cevallos-Torres, Jorge Charco-Aguirre, Leonel Vasquez-Cevallos
التنسيق: مقال
اللغة:الإنجليزية
منشور في: MDPI AG 2024-10-01
الموضوعات:
preeclampsia
bayesian network classifiers
class imbalance
oversampling
undersampling
SMOTE-NC
الوصول للمادة أونلاين:https://www.mdpi.com/2227-7390/12/21/3351

الانترنت

https://www.mdpi.com/2227-7390/12/21/3351

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