The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Mining data with random forests: A survey and results of new tests
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Transaction aggregation as a strategy for credit card fraud detection
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0116-z · Cited by 337
Cited by
Analysis of machine learning techniques for heart failure readmissions
Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., Negahban, S. N., & Krumholz, H. M. (2016) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.116.003039 · Cited by 401
Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017) · Big Data Research · doi:10.1016/j.bdr.2017.07.003 · Cited by 367
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
Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic
Nguyen, C., Wang, Y., & Nguyen, H. N. (2013) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2013.65070 · Cited by 310
Traffic accident detection using random forest classifier
Dogru, N., & Subasi, A. (2018) · 2018 15th Learning and Technology Conference (L&T) · doi:10.1109/lt.2018.8368509 · Cited by 308
Metadata
- Authors
- Verikas, A., Gelzinis, A., & Bacauskiene, M.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 44
- Issue
- 2
- Pages
- 330-349
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2034489756
- Cited by
- 719
- References
- 3
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mining data with random forests: A survey and results of new tests. https://app.cochat.ai/papers/10.1016/j.patcog.2010.08.011
- The paper
- Verikas, A., Gelzinis, A., & Bacauskiene, M. (2011). Mining data with random forests: A survey and results of new tests. Pattern Recognition. https://doi.org/10.1016/j.patcog.2010.08.011
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.
