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Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H. and Wu, S. (2004) Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decision Support Systems, 37, 543-558.
https://doi.org/10.1016/S0167-9236(03)00086-1

has been cited by the following article:

• TITLE: Research on P2P Credit Risk Assessment Model Based on RBM Feature Extraction—Take SME Customers as an Example

  AUTHORS: Jianhui Yang, Qiman Li, Dongsheng Luo

  KEYWORDS: P2P, Credit Evaluation Model, RBM Algorithm, Credit Risk

  JOURNAL NAME: Open Journal of Business and Management, Vol.7 No.4, August 7, 2019

  ABSTRACT: This paper combines the nonlinear dimensionality reduction method, and the Restricted Boltzmann machine (RBM algorithm), to assess the credit risk of P2P borrowers. After screening and processing many big data indicators, the most representative indicators are selected to build the P2P customer credit risk assessment model. In addition, after comparing the advantages and disadvantages of linear dimensionality reduction algorithm and nonlinear dimensionality reduction algorithm, this paper establishes a P2P enterprise customer credit risk assessment model based on RBM feature extraction combined with contrast divergence theory. It is concluded that the effect of RBM is better than that of PCA when the same model is selected. The Logistic model performs best in the three models when the same data feature extraction method is selected.