Classification of Credit Applicants Using SVM Variants Coupled with Filter-Based Feature Selection-Reference-Cited by-同舟云学术

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Classification of Credit Applicants Using SVM Variants Coupled with Filter-Based Feature Selection

Published:2022-09-01 Issue: Volume: Page:136-145
ISSN:2367-4512
Container-title:Lecture Notes on Data Engineering and Communications Technologies
language:
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Author:

Akil Siham,Sekkate Sara,Adib Abdellah

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-15191-0_13

Reference25 articles.

1. Olson, D.L., Wu, D.D.: Credit risk analysis. In: Enterprise Risk Management, pp. 117–136 (2015)

2. Siham, A., Sara, S., Abdellah, A.: Feature selection based on machine learning for credit scoring: an evaluation of filter and embedded methods. In: 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings (2021)

3. Chen, W., Li, Z., Guo, J.: A VNS-EDA Algorithm-Based Feature Selection for Credit Risk Classification. vol. 2020 (2020)

4. Lecture Notes in Networks and Systems;A Chakhtouna,2021

5. Huang, C.L., Chen, M.C., Wang, C.J.: Credit scoring with a data mining approach based on support vector machines. Expert Syst. Appl. 33(4), 847–856 (2007)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Credit Scoring Models with Monte Carlo Simulated Features;2024 IEEE 12th International Symposium on Signal, Image, Video and Communications (ISIVC);2024-05-21

2. A Synthesis on Machine Learning for Credit Scoring: A Technical Guide;Lecture Notes in Networks and Systems;2024

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