The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification
Abstract
Class imbalance learning tackles supervised learning problems where some classes have significantly more examples than others. Most of the existing research focused only on binary-class cases. In this paper, we study multiclass imbalance problems and propose a dynamic sampling method (DyS) for multilayer perceptrons (MLP). In DyS, for each epoch of the training process, every example is fed to the current MLP and then the probability of it being selected for training the MLP is estimated. DyS dynamically selects informative data to train the MLP. In order to evaluate DyS and understand its strength and weakness, comprehensive experimental studies have been carried out. Results on 20 multiclass imbalanced data sets show that DyS can outperform the compared methods, including pre-sample methods, active learning methods, cost-sensitive methods, and boosting-type methods.
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SMOTE synthetic minority over-sampling technique
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Individual Comparisons by Ranking Methods
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
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The class imbalance problem: A systematic study1
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A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
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Cited by
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
Training deep neural networks on imbalanced data sets
Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., & Kennedy, P. J. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727770 · Cited by 491
Deep learning in microscopy image analysis: a survey
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460
van Grinsven, M. J. J. P., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sanchez, C. I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526689 · Cited by 426
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Zhang, C., Tan, K. C., Li, H., & Hong, G. S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2832648 · Cited by 282
Metadata
- Authors
- Lin, M., Tang, K., & Yao, X.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2013-04
- Volume
- 24
- Issue
- 4
- Pages
- 647-660
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2083551746
- Cited by
- 165
- References
- 5
- FWCI
- 15.46
- Type
- journal article
- Language
- en
- Open access
- closed
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