Augmented neural networks for modelling consumer indebtness

Ladas, Alexandros, Garibaldi, Jonathan M., Scarpel, Rodrigo and Aickelin, Uwe (2014) Augmented neural networks for modelling consumer indebtness. In: 2014 International Joint Conference on Neural Networks (IJCNN), 6-11 July 2014, Beijing, China.

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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.

Item Type: Conference or Workshop Item (Paper)
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
Keywords: Data Mining, Digital Economy, Neural Networks, Regression
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 30 Sep 2014 11:05
Last Modified: 04 May 2020 16:54

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