The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
An instance level analysis of data complexity
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Cites
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
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.606 · Cited by 612
Cited by
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Lemaître, G., Nogueira, F., & Aridas, C. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.06570 · Cited by 1,514
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data
Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., Luo, S., Yang, X., & Reyes, M. C. (2021) · IEEE Access · doi:10.1109/access.2021.3102399 · Cited by 335
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Sampath, V., Maurtua, I., Aguilar Martín, J. J., & Gutierrez, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00414-0 · Cited by 259
Striking the Right Balance With Uncertainty
Khan, S., Hayat, M., Zamir, S. W., Shen, J., & Shao, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00019 · Cited by 180
Sha, L., Rakovic, M., Das, A., Gasevic, D., & Chen, G. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3196278 · Cited by 67
Metadata
- Authors
- Smith, M. R., Martinez, T., & Giraud-Carrier, C.
- Venue
- Machine Learning
- Published
- 2013-11-05
- Volume
- 95
- Issue
- 2
- Pages
- 225-256
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2022477494
- Cited by
- 436
- References
- 4
- FWCI
- 11.78
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Smith, M. R., Martinez, T., & Giraud-Carrier, C. (2014). An instance level analysis of data complexity. Machine Learning. https://doi.org/10.1007/s10994-013-5422-z
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.
