Credit risk prediction with and without weights of evidence using quantitative learning models-Reference-Cited by-同舟云学术

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Credit risk prediction with and without weights of evidence using quantitative learning models

Published:2024-04-15 Issue:1 Volume:12 Page:
ISSN:2332-2039
Container-title:Cogent Economics & Finance
language:en
Short-container-title:Cogent Economics & Finance

Author:

Seitshiro Modisane B.¹²^ORCID,Govender Seshni³^ORCID

Affiliation:

1. Centre for Business Mathematics and Informatics, North-West University, Potchefstroom, South Africa

2. National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch, South Africa

3. Department of Decision Sciences, University of South Africa, Pretoria, South Africa

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23322039.2024.2338971

Reference44 articles.

1. Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

2. Aggarwal, A., Kasiviswanathan, S., Xu, Z., Feyisetan, O., & Teissier, N. (2021 Label inference attacks from log-loss scores [Paper presentation]. International Conference on Machine Learning (pp. 120–129). PMLR.

3. Support Vector Machines for Classification

4. Bénédict G. Koops V. Odijk D. & de Rijke M. (2021). Sigmoidf1: A smooth f1 score surrogate loss for multilabel classification. arXiv preprint arXiv:2108.10566.

5. Classification and feature selection techniques in data mining;Beniwal S.;International Journal of Engineering Research & Technology (Ijert),2012

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