Title :
Weighted LS-SVM Credit Scoring Models with AUC Maximization by Direct Search
Author :
Zhou, Ligang ; Lai, Kin Keung
Author_Institution :
Dept. of Manage. Sci., City Univ. of Hong Kong, Hong Kong, China
Abstract :
Credit scoring models are very important tools for credit granting institutions to assess the credit risk of their customers. Most previous researches focus on improving predictive accuracy of models. In this research, a weighted LS-SVM credit scoring model with Area under ROC curve maximization is proposed and optimized by direct search. The tests on two real-world datasets show that it is effective for building the credit scoring model with good AUC performance.
Keywords :
finance; least squares approximations; optimisation; risk management; support vector machines; AUC maximization; ROC curve maximization; credit granting institutions; credit risk; direct search; weighted LS-SVM credit scoring models; Accuracy; Conference management; Costs; Genetic programming; Least squares methods; Predictive models; Risk management; Support vector machine classification; Support vector machines; Testing; AUC; Credit scoring; direct search;
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
DOI :
10.1109/CSO.2009.333
