What makes multi-class imbalanced problems difficult? An experimental study

Author:

Lango MateuszORCID,Stefanowski JerzyORCID

Funder

Horizon 2020

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference55 articles.

1. To combat multi-class imbalanced problems by means of over-sampling and boosting techniques;Abdi;Soft Computing,2015

2. Agrawal, A., Viktor, H. L., & Paquet, E. (2015). SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling. In 2015 7th International joint conference on knowledge discovery, knowledge engineering and knowledge management, Vol. 01 (pp. 226–234).

3. An empirical study for the multi-class imbalance problem with neural networks;Alejo,2008

4. Balancing strategies and class overlapping;Batista,2005

5. Calibrated resampling for imbalanced and long-tails in deep learning;Bellinger,2021

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