A Pruning Neural Network Model in Credit Classification Analysis

• Published in: Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 22
• Main Authors: Yu, Yang, Dai, Hongwei, Gao, Shangce, Ji, Junkai, Tang, Yajiao, Todo, Yuki
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Artificial neural networks; Back propagation; Bankruptcy; Classification; Computer engineering; Computer simulation; Computing time; Credit scoring; Decision making; Dendritic structure; Financial institutions; Humans; Logic circuits; Neural networks; Neural Networks (Computer); Neurons; Nonlinear systems; Pruning; Risk assessment; Synapses; China; Japan
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2018/9390410

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.