TY  - JOUR
ID  - SisLab1942
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1942/
IS  - 20
A1  - Ha, Van Sang
A1  - Nguyen, Ha Nam
A1  - Nguyen, Duc Nhan
Y1  - 2016/05//
N2  - Background/Objectives: This article presents a method of feature selection to improve the accuracy and the computation speed of credit scoring models. Methods/Analysis: In this paper, we proposed a credit scoring model based on parallel Random Forest classifier and feature selection method to evaluate the credit risks of applicants. By integration of Random Forest into feature selection process, the importance of features can be accurately evaluated to remove irrelevant and redundant features. Findings: In this research, an algorithm to  ...
JF  - Indian Journal of Science and Technology
VL  - 9
TI  - A Novel Credit Scoring Prediction Model based on Feature Selection Approach and Parallel Random Forest
AV  - none
ER  -