Hybrid clustering strategies for effective oversampling and undersampling in multiclass classification
Abstract Multiclass imbalance is a challenging problem in real-world datasets, where certain classes may have a low number of samples because they correspond to rare occurrences. To address the challenge of multiclass imbalance, this paper introduces a novel hybrid cluster-based oversampling and und...
Saved in:
| Main Authors: | Amirreza Salehi, Majid Khedmati |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84786-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An oversampling-undersampling strategy for large-scale data linkage
by: Hossein Hassani, et al.
Published: (2025-04-01) -
Hybridization of DEBOHID with ENN algorithm for highly imbalanced datasets
by: Sedat Korkmaz
Published: (2025-03-01) -
Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning
by: Haiou Qin
Published: (2025-01-01) -
GMO-AC: Gaussian-Based Minority Oversampling With Adaptive Outlier Filtering and Class Overlap Weighting
by: Seung Jee Yang, et al.
Published: (2024-01-01) -
IMCP: A Python package for imbalanced and multiclass data classifier performance comparison
by: Jesus S. Aguilar-Ruiz, et al.
Published: (2024-12-01)