eprintid: 1942
rev_number: 6
eprint_status: archive
userid: 300
dir: disk0/00/00/19/42
datestamp: 2016-11-24 08:58:17
lastmod: 2016-11-24 08:58:17
status_changed: 2016-11-24 08:58:17
type: article
metadata_visibility: show
creators_name: Ha, Van Sang
creators_name: Nguyen, Ha Nam
creators_name: Nguyen, Duc Nhan
creators_id: namnh@vnu.edu.vn
title: A Novel Credit Scoring Prediction Model based on Feature Selection Approach and Parallel Random Forest
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: 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  ...
date: 2016-05
date_type: published
full_text_status: none
publication: Indian Journal of Science and Technology
volume: 9
number: 20
refereed: TRUE
related_url_url: http://www.indjst.org/index.php/indjst/article/view/92299
related_url_type: pub
citation:   Ha, Van Sang and Nguyen, Ha Nam and Nguyen, Duc Nhan  (2016) A Novel Credit Scoring Prediction Model based on Feature Selection Approach and Parallel Random Forest.  Indian Journal of Science and Technology, 9  (20).