Learning from Imbalanced Data Sets
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Anomaly detection strategies excel when the minority class is highly disorganized and made up largely of noisy examples or small disjoints.
“When the minority class is highly disorganized and made up largely of noisy examples or small disjoints, anomaly detection strategies excel [17].”
From Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm (2024) · cites this paper as [17]
Cited by
Sorted by citations
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
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 · see citing works
Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., & Herrera, F. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105590 · Cited by 196 · see citing works
Self-citationData engineering for fraud detection
Baesens, B., Höppner, S., & Verdonck, T. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113492 · Cited by 170 · see citing works
Deng, Y., Lu, L., Aponte, L., Angelidi, A. M., Novak, V., Karniadakis, G. E., & Mantzoros, C. S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00480-x · Cited by 152 · see citing works
Gruson, D., Helleputte, T., Rousseau, P., & Gruson, D. (2019) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2019.04.013 · Cited by 125 · see citing works
Evolving techniques in cyber threat hunting: A systematic review
Mahboubi, A., Luong, K., Aboutorab, H., Bui, H. T., Jarrad, G., Bahutair, M., Camtepe, S., Pogrebna, G., Ahmed, E., Barry, B., et al. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.104004 · Cited by 94 · see citing works
Insider threat detection model using anomaly-based Isolation Forest algorithm
Al-Shehari, T., Al-Razgan, M., Alfakih, T., Alsowail, R. A., & Pandiaraj, S. (2023) · IEEE Access · doi:10.1109/access.2023.3326750 · Cited by 52 · see citing works
Al-Shehari, T. A., Rosaci, D., Al-Razgan, M., Alfakih, T., Kadrie, M., Afzal, H., & Nawaz, R. (2024) · IEEE Access · doi:10.1109/access.2024.3373694 · Cited by 51 · see citing works
“When the minority class is highly disorganized and made up largely of noisy examples or small disjoints, anomaly detection strategies excel [17].”
BackgroundChen, P.-W., Baune, N. A., Zwir, I., Wang, J., Swamidass, V., & Wong, A. W. K. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18041634 · Cited by 41 · see citing works
Rinderknecht, M. D., & Klopfenstein, Y. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00482-9 · Cited by 23 · see citing works
- Authors
- Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F.
- Affiliations
- Universidad de Granada (ES)
- Universidad Pública de Navarra (UPNA) (ES)
- Universidade Federal do ABC (BR)
- Virginia Commonwealth University (US)
- ORCID
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2018
- OpenAlex
- W4243367342
- Cited by
- 1,349
- References
- 0
- Citations by year
- 2026: 137 · 2025: 208 · 2024: 193 · 2023: 224 · 2022: 253 · 2021: 186 · 2020: 96 · 2019: 33 · 2018: 6 · 2017: 4 · 2016: 2 · 2014: 1 · 2012: 2
- FWCI
- 27.95
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- class imbalance, binary classification, skewed class distribution, performance indicators, machine learning classifiers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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