An Improving Majority Weighted Minority Oversampling Technique for Imbalanced Classification Problem

An Improving Majority Weighted Minority Oversampling Technique for Imbalanced Classification Problem

Minority oversampling techniques have played a pivotal role in the field of imbalanced learning. While traditional oversampling algorithms can cause problems such as intra-class imbalance of samples, ignoring important information of boundary samples, and high similarity between new and old samples....

Full description

Bibliographic Details
Main Authors: Chao-Ran Wang, Xin-Hui Shao
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Oversampling
boundary
minority sample
SMOTE
BIRCH
imbalanced learning
Online Access:https://ieeexplore.ieee.org/document/9311147/

Internet

https://ieeexplore.ieee.org/document/9311147/

Similar Items