The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Multiclass Imbalance Problems: Analysis and Potential Solutions
Abstract
Class imbalance problems have drawn growing interest recently because of their classification difficulty caused by the imbalanced class distributions. In particular, many ensemble methods have been proposed to deal with such imbalance. However, most efforts so far are only focused on two-class imbalance problems. There are unsolved issues in multiclass imbalance problems, which exist in real-world applications. This paper studies the challenges posed by the multiclass imbalance problems and investigates the generalization ability of some ensemble solutions, including our recently proposed algorithm AdaBoost.NC, with the aim of handling multiclass and imbalance effectively and directly. We first study the impact of multiminority and multimajority on the performance of two basic resampling techniques. They both present strong negative effects. "Multimajority" tends to be more harmful to the generalization performance. Motivated by the results, we then apply AdaBoost.NC to several real-world multiclass imbalance tasks and compare it to other popular ensemble methods. AdaBoost.NC is shown to be better at recognizing minority class examples and balancing the performance among classes in terms of G-mean without using any class decomposition.
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Cites
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Survey on deep learning with class imbalance
Johnson, J. M., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0192-5 · Cited by 3,032
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
Boosting methods for multi-class imbalanced data classification: an experimental review
Tanha, J., Abdi, Y., Samadi, N., Razzaghi, N., & Asadpour, M. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00349-y · Cited by 452
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Sampath, V., Maurtua, I., Aguilar Martín, J. J., & Gutierrez, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00414-0 · Cited by 259
M-AdaBoost-A based ensemble system for network intrusion detection
Zhou, Y., Mazzuchi, T. A., & Sarkani, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113864 · Cited by 99
Metadata
- Authors
- Wang, S., & Yao, X.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Published
- 2012-08
- Volume
- 42
- Issue
- 4
- Pages
- 1119-1130
- ISSN
- 1083-4419 (ISSN-L)
- 1941-0492
- OpenAlex
- W2024223694
- Cited by
- 567
- References
- 43
- FWCI
- 25.33
- Type
- journal article
- Language
- en
- Open access
- closed
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