Augmented neural networks for modelling consumer indebtness
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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Online Access: | https://eprints.nottingham.ac.uk/3350/ |
| _version_ | 1848791007010750464 |
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| author | Ladas, Alexandros Garibaldi, Jonathan M. Scarpel, Rodrigo Aickelin, Uwe |
| author_facet | Ladas, Alexandros Garibaldi, Jonathan M. Scarpel, Rodrigo Aickelin, Uwe |
| author_sort | Ladas, Alexandros |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:21:39Z |
| format | Conference or Workshop Item |
| id | nottingham-3350 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:39Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33502020-05-04T16:54:36Z https://eprints.nottingham.ac.uk/3350/ Augmented neural networks for modelling consumer indebtness Ladas, Alexandros Garibaldi, Jonathan M. Scarpel, Rodrigo Aickelin, Uwe 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. 2014-09-04 Conference or Workshop Item PeerReviewed 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. Data Mining Digital Economy Neural Networks Regression http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6889760 |
| spellingShingle | Data Mining Digital Economy Neural Networks Regression Ladas, Alexandros Garibaldi, Jonathan M. Scarpel, Rodrigo Aickelin, Uwe Augmented neural networks for modelling consumer indebtness |
| title | Augmented neural networks for modelling consumer indebtness |
| title_full | Augmented neural networks for modelling consumer indebtness |
| title_fullStr | Augmented neural networks for modelling consumer indebtness |
| title_full_unstemmed | Augmented neural networks for modelling consumer indebtness |
| title_short | Augmented neural networks for modelling consumer indebtness |
| title_sort | augmented neural networks for modelling consumer indebtness |
| topic | Data Mining Digital Economy Neural Networks Regression |
| url | https://eprints.nottingham.ac.uk/3350/ https://eprints.nottingham.ac.uk/3350/ |