Credit scoring model based on neural network with particle swarm optimization

Authors:
Liang Gao;Chi Zhou;Hai-Bing Gao;Yong-Ren Shi
Affiliations:
Department of Industrial & Manufacturing System Engineering, Huazhong Univ. of Sci. & Tech., Wuhan, China;Department of Industrial & Manufacturing System Engineering, Huazhong Univ. of Sci. & Tech., Wuhan, China;Department of Industrial & Manufacturing System Engineering, Huazhong Univ. of Sci. & Tech., Wuhan, China;Department of Industrial & Manufacturing System Engineering, Huazhong Univ. of Sci. & Tech., Wuhan, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 3
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks
Swarm intelligence

Swarm intelligence
A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

Expert Systems with Applications: An International Journal

Using data mining to improve assessment of credit worthiness via credit scoring models

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. This paper presents a Structure-tuning Particle Swarm Optimization (SPSO) approach for training feed-forward neural networks (NNs). The algorithm is successfully applied to a real credit problem. By simultaneously tuning the structure and connection weights of NNs, the proposed algorithm generates optimized NNs with problem-matched information processing capacity and it also eliminates some ill effects introduced by redundant input features and the corresponding redundant structure. Compared with BP and GA, SPSO can improve the pattern classification accuracy of NNs while speeding up the convergence of training process.