LOAN DEFAULT PREDICTION USING DIVERSIFIED SENSITIVITY UNDERSAMPLING

被引：0

作者：

Chen, Ya-Qi ^{[1
]}

Zhang, Jianjun ^{[1
]}

Ng, Wing W. Y. ^{[1
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangdong Prov Key Lab Computat Intelligence & Cy, Guangzhou, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2018年

基金：

中国国家自然科学基金;

关键词：

Imbalance data; Loan default prediction; P2P; Diversified sensitivity Undersampling (DSUS);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The loan default prediction is to predict rather the borrower will delay the repayment or not. This is an important problem for banking and finance companies. In this study, we focus on dealing with the data imbalance problem to enhance the performance of the loan default prediction. The approach in this study is a hybrid undersampling method that combines the clustering, the stochastic sensitivity measure and the radial basis function neural networks. A real loan default data from a P2P company in China is used to valid the performance of our method. Experiments results demonstrate that our approach yields a better generalization performance.

引用

页码：240 / 245

页数：6

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