The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Class imbalances versus small disjuncts
Abstract
It is often assumed that class imbalances are responsible for significant losses of performance in standard classifiers. The purpose of this paper is to the question whether class imbalances are truly responsible for this degradation or whether it can be explained in some other way. Our experiments suggest that the problem is not directly caused by class imbalances, but rather, that class imbalances may yield small disjuncts which, in turn, will cause degradation. We argue that, in order to improve classifier performance, it may, then, be more useful to focus on the small disjuncts problem than it is to focus on the class imbalance problem. We experiment with a method that takes the small disjunct problem into consideration, and show that, indeed, it yields a performance superior to the performance obtained using standard or advanced solutions to the class imbalance problem.
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Citations
Cites
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Self-citation
Cited by
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
Han, H., Wang, W.-Y., & Mao, B.-H. (2005) · Lecture notes in computer science · doi:10.1007/11538059_91 · Cited by 4,331
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088
“Cluster-based oversampling first clusters the dataset and then oversamples each cluster separately [36].”
BackgroundSurvey 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
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
Douzas, G., Bacao, F., & Last, F. (2018) · Information Sciences · doi:10.1016/j.ins.2018.06.056 · Cited by 1,277
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129
On the Class Imbalance Problem
Guo, X., Yin, Y., Dong, C., Yang, G., & Zhou, G. (2008) · 2008 Fourth International Conference on Natural Computation · doi:10.1109/icnc.2008.871 · Cited by 495
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310
Idris, A., Rizwan, M., & Khan, A. (2012) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2012.09.001 · Cited by 139
The effects of data sampling with deep learning and highly imbalanced big data
Johnson, J. M., & Khoshgoftaar, T. M. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-10022-7 · Cited by 81
ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification
Li, M., Xiong, A., Wang, L., Deng, S., & Ye, J. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105818 · Cited by 66
Detecting sexual predators in chats using behavioral features and imbalanced learning
CARDEI, C., & REBEDEA, T. (2017) · Natural Language Engineering · doi:10.1017/s1351324916000395 · Cited by 21
Metadata
- Authors
- Jo, T., & Japkowicz, N.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2004-06
- Volume
- 6
- Issue
- 1
- Pages
- 40-49
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2011376672
- Cited by
- 683
- References
- 1
- FWCI
- 10.17
- Type
- journal article
- Language
- en
- Open access
- closed
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