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MSMOTE: Improving Classification Performance When Training Data is Imbalanced
Abstract
Learning from data sets that contain very few instances of the minority class usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over the minority class. SMOTE (synthetic minority over-sampling technique) is specifically designed for learning from imbalanced data sets. This paper presents a modified approach (MSMOTE) for learning from imbalanced data sets, based on the SMOTE algorithm. MSMOTE not only considers the distribution of minority class samples, but also eliminates noise samples by adaptive mediation. The combination of MSMOTE and AdaBoost are applied to several highly and moderately imbalanced data sets. The experimental results show that the prediction performance of MSMOTE is better than SMOTEBoost in the minority class and F-values are also improved.
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Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
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Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381
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
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
Clustering-based undersampling in class-imbalanced data
Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017) · Information Sciences · doi:10.1016/j.ins.2017.05.008 · Cited by 832
Zhang, Y., Li, X., Gao, L., Wang, L., & Wen, L. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.04.005 · Cited by 226
Metadata
- Authors
- Hu, S., Liang, Y., Ma, L., & He, Y.
- Venue
- 2009 Second International Workshop on Computer Science and Engineering
- Publisher
- IEEE
- Published
- 2009
- Pages
- 13-17
- OpenAlex
- W2139986471
- Cited by
- 264
- References
- 1
- FWCI
- 1.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hu, S., Liang, Y., Ma, L., & He, Y. (2009). MSMOTE: Improving Classification Performance When Training Data is Imbalanced. 2009 Second International Workshop on Computer Science and Engineering. https://doi.org/10.1109/wcse.2009.756
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