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Augmented Neural Networks for modelling consumer indebtness

Ladas, Alexandros; M. Garibaldi, Jonathan; Scarpel, Rodrigo; Aickelin, Uwe

Authors

Alexandros Ladas

Rodrigo Scarpel

Uwe Aickelin



Abstract

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show that Computational Intelligence can offer a more holistic approach that is more suitable for the complex relationships an indebtness dataset has and Linear Regression cannot uncover. In particular, as our results show, Neural Networks achieve the best performance in modelling consumer indebtness, especially when they manage to incorporate the significant and experimentally verified results of the Data Mining process in the model, exploiting the flexibility Neural Networks offer in designing their topology. This novel method forms an elaborate framework to model Consumer indebtness that can be extended to any other real world application.

Journal Article Type Conference Paper
Start Date Jul 6, 2014
Publication Date Sep 4, 2014
Journal Proceedings of International Joint Conference on Neural Networks
Print ISSN 2161-4393
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 3086-3093
APA6 Citation Ladas, A., M. Garibaldi, J., Scarpel, R., & Aickelin, U. (2014). Augmented Neural Networks for modelling consumer indebtness. Proceedings of International Joint Conference on Neural Networks, 3086-3093. https://doi.org/10.1109/IJCNN.2014.6889760
DOI https://doi.org/10.1109/IJCNN.2014.6889760
Keywords Data Mining, Digital Economy, Neural Networks, Regression
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6889760
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2014 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ : IEEE, 2014. (ISBN: 9781467347013), pp. 3086-3093, (doi: 10.1109/IJCNN.2014.6889760 ). © 2014 IEEE

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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