Modified resampling strategy for extreme values in imbalanced air pollution data using moving block bootstrapping approach with relevance weighting (MBB-RW)

Abstract Air pollution datasets typically exhibit a right-skewed distribution. These conditions are caused by the presence of extreme events leading to imbalanced data distribution. This imbalanced regression poses a notable challenge in predictive modeling, as the models tend to be biased towards f...

وصف كامل

التفاصيل البيبلوغرافية

المؤلفون الرئيسيون:	Mahiran Muhammad, Ahmad Zia Ul-Saufie, Noor Fadhilah Ahmad Radi, Norazian Mohamed Noor, Arief Gusnanto
التنسيق:	Artigo
اللغة:	Inglês
منشور في:	Nature Portfolio 2025-12-01
سلاسل:	Scientific Reports
الموضوعات:	Particulate matter 10 (PM10) Extreme events Imbalanced data Moving block bootstrapping (MBB) Moving block bootstrapping with relevance weighting (MBB-RW) Extreme gradient boosting (XGBoost)
الوصول للمادة أونلاين:	https://doi.org/10.1038/s41598-025-28416-5
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!