Augmented Neural Networks for Modelling Consumer Indebtness
| Autor: | Uwe Aickelin, Rodrigo Arnaldo Scarpel, Alexandros Ladas, Jonathan M. Garibaldi |
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| Rok vydání: | 2014 |
| Předmět: |
FOS: Computer and information sciences
Computer science Process (engineering) media_common.quotation_subject Computational intelligence Machine learning computer.software_genre Machine Learning (cs.LG) Computational Engineering Finance and Science (cs.CE) Knowledge extraction Order (exchange) Debt Neural and Evolutionary Computing (cs.NE) Computer Science - Computational Engineering Finance and Science media_common Flexibility (engineering) Artificial neural network business.industry Computer Science - Neural and Evolutionary Computing Consumer debt Statistical model Industrial engineering Computer Science - Learning Artificial intelligence business computer |
| Zdroj: | IJCNN |
| DOI: | 10.48550/arxiv.1409.1057 |
| Popis: | 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. Comment: Proceedings of the 2014 World Congress on Computational Intelligence (WCCI 2014), pp. 3086-3093, 2014 |
| Databáze: | OpenAIRE |
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