The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
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Citations
Cites
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Neural Networks: A Comprehensive Foundation
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Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Classification and Regression Trees.
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Cited by
On hyperparameter optimization of machine learning algorithms: Theory and practice
Yang, L., & Shami, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.061 · Cited by 3,232
A comparative analysis of gradient boosting algorithms
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09896-5 · Cited by 2,998
An up-to-date comparison of state-of-the-art classification algorithms
Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440
Self-citationStatistical 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
Nweke, H. F., Teh, Y. W., Mujtaba, G., & Al-garadi, M. A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.002 · Cited by 394
Metadata
- Authors
- Xia, Y., Liu, C., Li, Y., & Liu, N.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 78
- Pages
- 225-241
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2586297576
- Cited by
- 769
- References
- 69
- FWCI
- 159.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2017.02.017
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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.
