Title :
Evaluation of Residential Loan by Combining RVM and Logistic Regression
Author_Institution :
Financial Dept., Wuhan Univ. of Technol., Wuhan, China
Abstract :
A combining forecast model is proposed to evaluate the residential loan, which improves the accuracy of a single evaluation model. Firstly, the Relevance Vector Machine (RVM) model and logistic regression model are trained by the financial data respectively. Then the weighted average rule is used to fuse these two models based on a weight training procedure. Finally, the combining model is employed to evaluate the real house loan data. The experiments show that the combining evaluation modal is super to a single model and behaves robust.
Keywords :
financial data processing; learning (artificial intelligence); regression analysis; RVM; combining forecast model; financial data; logistic regression model; real house loan data; relevance vector machine; residential loan evaluation; weight training procedure; weighted average rule; Accuracy; Data models; Logistics; Mathematical model; Predictive models; Support vector machines; Training;
Conference_Titel :
Management and Service Science (MASS), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6579-8
DOI :
10.1109/ICMSS.2011.5998124
