Research Article
Loan Prepayment Prediction Based on SVM-RFE and XGBoost Models
@INPROCEEDINGS{10.4108/eai.17-6-2022.2322765, author={Qi Mao and Gang Liu and Zhiyu Chen and Jianwei Guo and Peng Liu}, title={Loan Prepayment Prediction Based on SVM-RFE and XGBoost Models}, proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2022}, month={10}, keywords={loan prepayment; feature selection; svm-rfe; xgboost; weighted cross-entropy loss function}, doi={10.4108/eai.17-6-2022.2322765} }- Qi Mao
Gang Liu
Zhiyu Chen
Jianwei Guo
Peng Liu
Year: 2022
Loan Prepayment Prediction Based on SVM-RFE and XGBoost Models
ICIDC
EAI
DOI: 10.4108/eai.17-6-2022.2322765
Abstract
The problem of large dimensionality of loan data and unbalanced data samples severely affects the classification, and the article proposes a support vector machine for feature recursive elimination and XGBoost for a loan early repayment prediction. Firstly, the combination of Pearson index and SVM-RFE in the data feature layer can reduce the dimension of data, find the best feature subset including more information, and then find more information. Secondly, the weighted cross-entropy loss function is introduced into the XGBoost algorithm to solve the problem of data imbalance. Finally, a comparative experiment is carried out on the LendingClub data set to confirm the effectiveness of the proposed model in predicting and analyzing the personal behavior of loan prepayment.