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Instance selection and optimization of neural networks


Zhao, Z and Xu, S and Kang, BH and Kabir, M and Liu, Y, Instance selection and optimization of neural networks, Proceedings of the 9th International Conference on Information Technology and Applications, 1-4 July 2014, Sydney, Australia, pp. 1-6. ISBN 978-0-9803267-6-5 (2014) [Refereed Conference Paper]

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Credit scoring is an important tool in financial institutions, which can be used in credit granting decision. Credit applications are marked by credit scoring models and those with high marks will be treated as "good", while those with low marks will be regarded as "bad". As the data mining technique develops, automatic credit scoring systems are warmly welcomed for their high efficiency and objective judgments. Many machine learning algorithms have been applied on training credit scoring models, and ANN is one of them with good performance. This paper presents a higher accuracy credit scoring model based on MLP neural networks trained with back propagation algorithm. Our work focus on enhancing credit scoring models in 3 aspects: optimise data distribution in datasets using a new method called Average Random Choosing; compare effects of training-validation-test instances numbers; and find the most suitable number of hidden units. Another contribution of this paper is summarising the tendency of scoring accuracy of models when the number of hidden units increases. The experiment results show that our methods can achieve high credit scoring accuracy with imbalanced datasets. Thus, credit granting decision can be made by data mining methods using MLP neural networks.

Item Details

Item Type:Refereed Conference Paper
Keywords:back propagation, credit scoring, multilayer perceptron, neural network
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, M (Mr Mir Kabir)
ID Code:96360
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2014-10-31
Last Modified:2018-03-18

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