A Denoising Autoencoder Approach for Credit Risk Analysis-Reference-Cited by-同舟云学术

A Denoising Autoencoder Approach for Credit Risk Analysis

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the 2018 International Conference on Computing and Artificial Intelligence - ICCAI 2018
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Author:

Fan Qi¹,Yang Jiasheng²

Affiliation:

1. Department of Financial Department, Nankai University, Tianjin, China

2. Department of Human, Society and Mathematics, University of Southampton, University of Southampton, Highfield, Southampton, United Kingdom

Publisher

ACM Press

Reference11 articles.

1. Kou, Gang, Yi Peng, and Guoxun Wang. "Evaluation of clustering algorithms for financial risk analysis using MCDM methods." Information Sciences 275 (2014): 1--12.

2. Gan, Qiwei, Binjie Luo, and Zhangxi Lin. "Risk management of residential mortgage in China using data mining a case study." In New Trends in Information and Service Science, 2009. NISS'09. International Conference on, pp. 1378--1383. IEEE, 2009.

3. Vincent, Pascal, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion." Journal of Machine Learning Research 11, no. Dec (2010): 3371--3408.

4. Dauphin, Grégoire Mesnil Yann, Xavier Glorot, Salah Rifai, Yoshua Bengio, Ian Goodfellow, Erick Lavoie, Xavier Muller et al. "Unsupervised and transfer learning challenge: a deep learning approach." In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 97--110. 2012.

5. Yu, Lean, Zebin Yang, and Ling Tang. "A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment." Flexible Services and Manufacturing Journal 28, no. 4 (2016): 576--592.

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