Abstract—Credit risk evaluation (CRE) is a very challenging and important management science problem in the domain of financial analysis. Many popular methods have been applied to tackle this problem in recent years. However, feature extraction and imbalanced data problem have not been paid enough attention in the current research, which play significant function in field of CRE. In this paper, we employed a deep learning approach to extract effective features and under-sampling technique to balance dataset. Our model combine under-sampling technique, Deep Boltzmann Machine (DBM) and Discriminative Restricted Boltzmann Machine (DRBM) method. To examine the performance, real world credit data of Lending Club is applied in the proposed model. The stable and better performance results show that the Hybrid classifier we propose is more effective and powerful.
Index Terms—Credit risk evaluation, deep Boltzmann machine, discriminative restricted Boltzmann machine, hybrid classifier.
Chong Wu and Dekun Gao are with the School of Management, Harbin Institute of Technology, Harbin, 150001 China (e-mail: firstname.lastname@example.org, email@example.com). Siyuan Xu is with the Beijing-Dublin International College, Beijing University of Technology, Beijing, 100000 China (e-mail: firstname.lastname@example.org).
Cite: Chong Wu, Dekun Gao, and Siyuan Xu, "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 182-187, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).