The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Addressing imbalance in multilabel classification: Measures and random resampling algorithms
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Citations
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
ML-KNN: A lazy learning approach to multi-label learning
Zhang, M.-L., & Zhou, Z.-H. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.12.019 · Cited by 3,552
A Review on Multi-Label Learning Algorithms
Zhang, M.-L., & Zhou, Z.-H. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.39 · Cited by 3,321
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
Learning multi-label scene classification
Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.03.009 · Cited by 2,296
García, S., Fernández, A., Luengo, J., & Herrera, F. (2010) · Information Sciences · doi:10.1016/j.ins.2009.12.010 · Cited by 2,229
Self-citationEditorial: special issue on learning from imbalanced data sets
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,063
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
Duygulu, P., Barnard, K., de Freitas, J. F. G., & Forsyth, D. A. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47979-1_7 · Cited by 1,608
López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013) · Information Sciences · doi:10.1016/j.ins.2013.07.007 · Cited by 1,590
Self-citationTsoumakas, G., Katakis, I., & Vlahavas, I. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_34 · Cited by 1,410
Cited by
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
Big data preprocessing: methods and prospects
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
Self-citationDiez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.10.005 · Cited by 633
A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions
Kaur, H., Pannu, H. S., & Malhi, A. K. (2020) · ACM Computing Surveys · doi:10.1145/3343440 · Cited by 488
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
Metadata
- Authors
- Charte, F., Rivera, A. J., del Jesus, M. J., & Herrera, F.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 163
- Pages
- 3-16
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2133223948
- Cited by
- 288
- References
- 41
- FWCI
- 14.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Charte, F., Rivera, A. J., del Jesus, M. J., & Herrera, F. (2015). Addressing imbalance in multilabel classification: Measures and random resampling algorithms. Neurocomputing. https://doi.org/10.1016/j.neucom.2014.08.091
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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.
