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Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 9390410, 22 pages
Research Article

A Pruning Neural Network Model in Credit Classification Analysis

1Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
2College of Economics, Central South University of Forestry and Technology, Changsha 410004, China
3School of Computer Engineering, Huaihai Institute of Technology, Lianyungang 222005, China
4School of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi 920-1192, Japan

Correspondence should be addressed to Shangce Gao

Received 8 November 2017; Revised 11 January 2018; Accepted 14 January 2018; Published 11 February 2018

Academic Editor: Pietro Aricò

Copyright © 2018 Yajiao Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs) to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.