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Investigation of multilayer perceptron and class imbalance problems for credit rating


Zhao, Z and Xu, S and Kang, BH and Kabir, MMJ and Liu, Y, Investigation of multilayer perceptron and class imbalance problems for credit rating, International Journal of Computer and Information Technology, 3, (4) pp. 805-812. ISSN 2279-0764 (2014) [Refereed Article]

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Copyright 2014 International Journal of Computer and Information Technology

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Multilayer perceptron (MLP) neural network is widely used in automatic credit scoring systems with high accuracies and efficiencies. However, class imbalance problems severely harm the prediction accuracy, when the number of instances in one class greatly overweighs the other class. In credit scoring datasets, class imbalance problems exist in fault detection models since there are always less unqualified cases than approved applications. In this work, we investigate the affection of different MLP structure to the prediction ability and develop a novel instance selection method to solve class imbalance problems in German credit datasets. We train 34 models 20 times with different initial weights and training instances. Each model has 6 to 39 hidden units in one hidden layer. Our test results prove that the prediction accuracy of the optimized model with our new instance selection methods is 5% higher than the best result reported in the relevant literature of recent years. We also summarize the tendency of scoring accuracy when the numbers of hidden units in MLP increases. The results of this work can be applied not only for credit scoring, but also in other MLP neural network applications, especially when the distribution of instances in a dataset is imbalanced.

Item Details

Item Type:Refereed Article
Keywords:neural network, multilayer perceptron, credit rating, instance selection, attribute selection, data mining
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Zhao, Z (Dr Zongyuan Zhao)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Kang, BH (Professor Byeong Kang)
UTAS Author:Kabir, MMJ (Mr Mir Kabir)
ID Code:93774
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2014-08-15
Last Modified:2017-11-18

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