Minimizing the Overlapping Degree to Improve Class-Imbalanced Learning Under Sparse Feature Selection: Application to Fraud Detection

Minimizing the Overlapping Degree to Improve Class-Imbalanced Learning Under Sparse Feature Selection: Application to Fraud Detection

In recent years, the classification of class-imbalanced data has obtained increasing attention across different scientific areas such as fraud detection, metabolomics, Cancer diagnosis, etc. This interest comes after proving the negative effect of overlapping on the performance of class-imbalanced l...

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Bibliographic Details
Main Authors: El Barakaz Fatima, Boutkhoum Omar, El Moutaouakkil Abdelmajid, Furqan Rustam, Arif Mehmood, Gyu Sang Choi
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Augmented R-value
class-imbalance
feature selection
fraud detection
overlapping
Online Access:https://ieeexplore.ieee.org/document/9343840/

Internet

https://ieeexplore.ieee.org/document/9343840/

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