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Instance selection and optimization of neural networks
conference contribution
posted on 2023-05-23, 09:29 authored by Zhao, Z, Shuxiang XuShuxiang Xu, Byeong KangByeong Kang, Kabir, M, Liu, YCredit 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.
History
Publication title
Proceedings of the 9th International Conference on Information Technology and ApplicationsPagination
1-6ISBN
978-0-9803267-6-5Department/School
School of Information and Communication TechnologyPublisher
IEEE-Inst Electrical Electronics Engineers IncPlace of publication
New York, USAEvent title
9th International Conference on Information Technology and ApplicationsEvent Venue
Sydney, AustraliaDate of Event (Start Date)
2014-07-01Date of Event (End Date)
2014-07-04Rights statement
Copyright 2014 ICITARepository Status
- Restricted