The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Experimental perspectives on learning from imbalanced data
Abstract
We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the perspective of reduced performance. Can data sampling be used to improve the performance of learners built from imbalanced data? Is the effectiveness of sampling related to the type of learner? Do the results change if the objective is to optimize different performance metrics? We address these and other issues in this work, showing that sampling in many cases will improve classifier performance.
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Cites
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Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
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Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.
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Experimentation in Software Engineering: An Introduction
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Experimentation in Software Engineering
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Cited by
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309
Survey on deep learning with class imbalance
Johnson, J. M., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0192-5 · Cited by 3,032
Self-citationA 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
Data mining for credit card fraud: a comparative study
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.008 · Cited by 1,108
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
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Metadata
- Authors
- Van Hulse, J., Khoshgoftaar, T. M., & Napolitano, A.
- Venue
- Proceedings of the 24th international conference on Machine learning
- Publisher
- ACM
- Published
- 2007-06-20
- Pages
- 935-942
- OpenAlex
- W2119168155
- Cited by
- 786
- References
- 20
- FWCI
- 14.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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