A hybrid model for credit evaluation problem

Authors:
Hui Fu;Xiaoyong Liu
Affiliations:
Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China;Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

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Abstract

This paper provides a novel hybrid model to solve credit scoring problems. This model is based on RBF neural network with genetic algorithm and its principal character is that Central position, center spread and weights of RBF neural network are encode as genes of Chromosome in genetic algorithm. And then using genetic algorithm trains RBF neural network circularly. A real world credit dataset in the University of California Irvine Machine Learning Repository are selected for the experiment. Numerical experiment shows that the model possesses fast learning ability and excellent generalization ability, and verifies that the novel model has better preference.