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A Novel Credit Scoring Prediction Model based on Feature Selection Approach and Parallel Random Forest

Van Sang Ha and Ha Nam Nguyen and Duc Nhan Nguyen (2016) A Novel Credit Scoring Prediction Model based on Feature Selection Approach and Parallel Random Forest. Indian Journal of Science and Technology, 9 (20).

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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 ...

Item Type:Article
Subjects:Information Technology (IT)
Divisions:Faculty of Information Technology (FIT)
ID Code:1942
Deposited By: Dr Hà Nam Nguyễn
Deposited On:24 Nov 2016 08:58
Last Modified:24 Nov 2016 08:58

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