Credit scoring based on semi-supervised generalized additive logistic regression
FANG Kuangnan1,2, CHEN Zilan3
1. Department of Statistics, School of Economics, Xiamen University, Xiamen 361000, China; 2. The MOE Key Laboratory of Econometrics(Xiamen University), Xiamen 361000, China; 3. Student Affairs Office, Xiamen University, Xiamen 361000, China
Abstract：The traditional credit scoring model is mainly based on the supervised learning method. However, in the actual loan problem, the labeled sample information is often acquired at a higher cost and longer cycle, while the unlabeled sample information is abundant. In order to make full use of the information of unlabeled samples in modeling, this paper proposes a credit scoring model based on semi-supervised generalized additive (SSGA) logistic regression. The model can not only use both labeled and unlabeled sample information, but also realize the estimation of model parameters and the selection of significant variables. The simulation experiments show that the proposed model has significantly better performance in extrapolation prediction and variable selection than the supervised model. Finally, the model is applied to the assessment of personal credit loan default risk.
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2020, Vol. 40 
