A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets

A Multi-Schematic Classifier-Independent Oversampling Approach for Imbalanced Datasets

Labelled imbalanced data, used for classification problems, have an unequal distribution of samples over the classes. Traditional classification models, such as random forest, gradient boosting, face a problem when dealing with imbalanced datasets. Over 85 oversampling algorithms, mostly extensions...

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Bibliographic Details
Main Authors: Saptarshi Bej, Kristian Schulz, Prashant Srivastava, Markus Wolfien, Olaf Wolkenhauer
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Imbalanced datasets
LoRAS
oversampling
SMOTE
Online Access:https://ieeexplore.ieee.org/document/9524579/

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