Loan Default Prediction with Deep Learning and Muddling Label Regularization-Reference-Cited by-同舟云学术

Loan Default Prediction with Deep Learning and Muddling Label Regularization

Published:2022-07-01 Issue:7 Volume:E105.D Page:1340-1342
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

JIANG Weiwei¹

Affiliation:

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/7/E105.D_2022EDL8003/_pdf

Reference7 articles.

1. [1] L. Coenen, W. Verbeke, and T. Guns, “Machine learning methods for short-term probability of default: A comparison of classification, regression and ranking methods,” Journal of the Operational Research Society, vol.73, no.1, pp.191-206, 2022. 10.1080/01605682.2020.1865847

2. [2] W. Jiang, “Applications of deep learning in stock market prediction: Recent progress,” Expert Systems with Applications, vol.184, 115537, 2021. 10.1016/j.eswa.2021.115537

3. [3] W. Jiang and L. Zhang, “Edge-SiamNet and Edge-TripleNet: New deep learning models for handwritten numeral recognition,” IEICE Trans. Inf. & Syst., vol.103, no.3, pp.720-723, March 2020. 10.1587/transinf.2019edl8199

4. [4] R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,” Information Fusion, vol.81, pp.84-90, 2022. 10.1016/j.inffus.2021.11.011

5. [5] K. Lounici, K. Meziani, and B. Riu, “Muddling label regularization: Deep learning for tabular datasets,” arXiv preprint arXiv:2106.04462, 2021.

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