Designing neural networks for modeling biological data: A statistical perspective
In this paper, we propose a strategy for the selection of the hidden layer size in feedforward neural network models. The procedure herein presented is based on comparison of different models in terms of their out of sample predictive ability, for a specified loss function. To overcome the problem...
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| Format: | Article |
| Language: | English |
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AIMS Press
2013-09-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.331 |
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| _version_ | 1832590161759698944 |
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| author | Michele La Rocca Cira Perna |
| author_facet | Michele La Rocca Cira Perna |
| author_sort | Michele La Rocca |
| collection | DOAJ |
| description | In this paper, we propose a strategy for the selection of the hidden layer size in feedforward neural network models. The procedure herein presented is based on comparison of different models in terms of their out of sample predictive ability, for a specified loss function. To overcome the problem of data snooping, we extend the scheme based on the use of the reality check with modifications apt to compare nested models. Some applications of the proposed procedure to simulated and real data sets show that it allows to select parsimonious neural network models with the highest predictive accuracy. |
| format | Article |
| id | doaj-art-fff5244de7de4cc18c288a4ab3eeed60 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2013-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-fff5244de7de4cc18c288a4ab3eeed602025-01-24T02:28:02ZengAIMS PressMathematical Biosciences and Engineering1551-00182013-09-0111233134210.3934/mbe.2014.11.331Designing neural networks for modeling biological data: A statistical perspectiveMichele La Rocca0Cira Perna1Department of Economics and Statistics - University of Salerno, Via Giovanni Paolo II, 132. 84084 Fisciano (SA)Department of Economics and Statistics - University of Salerno, Via Giovanni Paolo II, 132. 84084 Fisciano (SA)In this paper, we propose a strategy for the selection of the hidden layer size in feedforward neural network models. The procedure herein presented is based on comparison of different models in terms of their out of sample predictive ability, for a specified loss function. To overcome the problem of data snooping, we extend the scheme based on the use of the reality check with modifications apt to compare nested models. Some applications of the proposed procedure to simulated and real data sets show that it allows to select parsimonious neural network models with the highest predictive accuracy.https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.331multiple testingpredictive abilitybootstrap.neural networks |
| spellingShingle | Michele La Rocca Cira Perna Designing neural networks for modeling biological data: A statistical perspective Mathematical Biosciences and Engineering multiple testing predictive ability bootstrap. neural networks |
| title | Designing neural networks for modeling biological data: A statistical perspective |
| title_full | Designing neural networks for modeling biological data: A statistical perspective |
| title_fullStr | Designing neural networks for modeling biological data: A statistical perspective |
| title_full_unstemmed | Designing neural networks for modeling biological data: A statistical perspective |
| title_short | Designing neural networks for modeling biological data: A statistical perspective |
| title_sort | designing neural networks for modeling biological data a statistical perspective |
| topic | multiple testing predictive ability bootstrap. neural networks |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.331 |
| work_keys_str_mv | AT michelelarocca designingneuralnetworksformodelingbiologicaldataastatisticalperspective AT ciraperna designingneuralnetworksformodelingbiologicaldataastatisticalperspective |