The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
A novel ensemble method for classifying imbalanced data
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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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An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005
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
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
Effective data generation for imbalanced learning using conditional generative adversarial networks
Douzas, G., & Bacao, F. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.030 · Cited by 601
A review of methods for imbalanced multi-label classification
Tarekegn, A. N., Giacobini, M., & Michalak, K. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107965 · Cited by 426
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition
Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2719043 · Cited by 346
Metadata
- Authors
- Sun, Z., Song, Q., Zhu, X., Sun, H., Xu, B., & Zhou, Y.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 48
- Issue
- 5
- Pages
- 1623-1637
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2091007025
- Cited by
- 414
- References
- 80
- FWCI
- 20.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Sun, Z., Song, Q., Zhu, X., Sun, H., Xu, B., & Zhou, Y. (2015). A novel ensemble method for classifying imbalanced data. Pattern Recognition. https://doi.org/10.1016/j.patcog.2014.11.014
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
