An Oversampling Method for Class Imbalance Problems on Large Datasets
Several oversampling methods have been proposed for solving the class imbalance problem. However, most of them require searching the k-nearest neighbors to generate synthetic objects. This requirement makes them time-consuming and therefore unsuitable for large datasets. In this paper, an oversampli...
Main Authors: | Carrasco-Ochoa, J.A (Author), Martínez-Trinidad, J.F (Author), Rodríguez-Torres, F. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI
2022
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets
by: Saptarshi Bej, et al.
Published: (2021-01-01) -
Beyond the Boundaries of SMOTE: A Framework for Manifold-based Synthetic Oversampling
by: Bellinger, Colin
Published: (2016) -
Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance
by: Kiran Kumar Bejjanki, et al.
Published: (2020-03-01) -
An Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification
by: Yong Zhang, et al.
Published: (2021-01-01) -
A Synthetic Minority Based on Probabilistic Distribution (SyMProD) Oversampling for Imbalanced Datasets
by: Intouch Kunakorntum, et al.
Published: (2020-01-01)