Dynamic credit scoring using B & B with incremental-SVM-ensemble
Abstract
Purpose
Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring.
Design/methodology/approach
This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring.
Findings
The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model.
Originality/value
This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.
Keywords
Acknowledgements
This research is partially supported by the National Natural Science Foundation of China (Nos 71371171, 71171179), the Humanities and Social Science Foundation of Ministry of Education of China (No. 13YJC630140), and the Zhejiang Provincial Natural Science Foundation of China (Nos LY13G010001, LR13G010001). The authors gratefully thank anonymous referees for their useful comments and editors for their work.
Citation
Sun, J., Li, H., Chang, P.-C. and Huang, Q.-H. (2015), "Dynamic credit scoring using B & B with incremental-SVM-ensemble", Kybernetes, Vol. 44 No. 4, pp. 518-535. https://doi.org/10.1108/K-02-2014-0036
Publisher
:Emerald Group Publishing Limited
Copyright © 2015, Emerald Group Publishing Limited