Biased resampling strategies for imbalanced spatio-temporal forecasting

2021/06/21 by Mariana Oliveira, Nuno Moniz, Luı́s Torgo +3 · 7 citations
Computer Science · Decision Sciences · Medicine · #Artificial intelligence #Computer science #Data mining #Data-Driven Disease Surveillance #Forecasting Techniques and Applications #Imbalanced Data Classification Techniques #Machine learning #Relevance (law) #Resampling #Sampling (signal processing) #Set (abstract data type)

paper · doi:10.1007/s41060-021-00256-2

published in International Journal of Data Science and Analytics 12(3), 205-228 (Springer International Publishing)

crossref issued 2021/06/21 · crossref published 2021/06/21 · crossref published-online 2021/06/21 · openalex publication_date 2021/06/21 · crossref created 2021/06/21 · crossref published-print 2021/09/01 · crossref deposited 2023/01/01 · crossref indexed 2025/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Citations

• Nonnegativity-, monotonicity-, or convexity-preserving cubic and quintic Hermite interpolation
• UBL: an R package for Utility-based Learning
• Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
• Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
• SMOTE: Synthetic Minority Over-sampling Technique

Cited by

• Oversampling and undersampling in data analysis [wikipedia]