The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Credit scoring using the clustered support vector machine
Open accessAbstract
This study introduces the use of the clustered support vector machine (CSVM) for credit scoring.The CSVM has been shown to relax size constraints while remaining highly accurate.The results suggest that the CSVM is a useful alternative to kernel SVM approaches when training datasets get large. This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally.
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Cited by
Emerging Artificial Intelligence Methods in Structural Engineering
Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
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Statistical and machine learning models in credit scoring: A systematic literature survey
Dastile, X., Celik, T., & Potsane, M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106263 · Cited by 404
Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks
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Can Web Search Queries Predict Prices Change on the Real Estate Market?
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Metadata
- Authors
- Harris, T.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 42
- Issue
- 2
- Pages
- 741-750
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1994085451
- Cited by
- 269
- References
- 39
- FWCI
- 78.31
- Type
- journal article
- Language
- en
- Open access
- green
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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.
