The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
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 class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Using AUC and accuracy in evaluating learning algorithms
Huang, J., & Ling, C. X. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.50 · Cited by 2,148
Dataset Shift in Machine Learning
(2008) · doi:10.7551/mitpress/9780262170055.001.0001 · Cited by 1,553
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
KBA: kernel boundary alignment considering imbalanced data distribution
Wu, G., & Chang, E. Y. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.95 · Cited by 380
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
A survey on addressing high-class imbalance in big data
Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018) · Journal of Big Data · doi:10.1186/s40537-018-0151-6 · Cited by 759
A review of artificial neural network models for ambient air pollution prediction
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.06.014 · Cited by 459
A novel ensemble method for classifying imbalanced data
Sun, Z., Song, Q., Zhu, X., Sun, H., Xu, B., & Zhou, Y. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2014.11.014 · Cited by 414
On the use of MapReduce for imbalanced big data using Random Forest
del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.043 · Cited by 328
Self-citation
Metadata
- Authors
- López, V., Fernández, A., Moreno-Torres, J. G., & Herrera, F.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 39
- Issue
- 7
- Pages
- 6585-6608
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2088059023
- Cited by
- 316
- References
- 9
- FWCI
- 13.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- López, V., Fernández, A., Moreno-Torres, J. G., & Herrera, F. (2012). Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2011.12.043
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.
