The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Credit scoring with a data mining approach based on support vector machines
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Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Neural network credit scoring models
West, D. (2000) · Computers & Operations Research · doi:10.1016/s0305-0548(99)00149-5 · Cited by 1,033
Cited by
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167
“In industrial settings, the problem often concerns the predictive performance or other qualities of a model, such as how to assign credit scores with high accuracy (26), or do so fairly with respect to gender and race (16).”
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364
Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284
Consumer credit-risk models via machine-learning algorithms
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.06.001 · Cited by 864
Unique in the shopping mall: On the reidentifiability of credit card metadata
de Montjoye, Y.-A., Radaelli, L., Singh, V. K., & Pentland, A. “. (2015) · Science · doi:10.1126/science.1256297 · Cited by 570
Metadata
- Authors
- Huang, C.-L., Chen, M.-C., & Wang, C.-J.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2007-11
- Volume
- 33
- Issue
- 4
- Pages
- 847-856
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2103780778
- Cited by
- 804
- References
- 5
- FWCI
- 83.59
- 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.
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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: Credit scoring with a data mining approach based on support vector machines. https://app.cochat.ai/papers/10.1016/j.eswa.2006.07.007
- The paper
- Huang, C.-L., Chen, M.-C., & Wang, C.-J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2006.07.007
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.
