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Mathematical Biosciences and Engineering

2014, Volume 11, Issue 2: 331-342. doi: 10.3934/mbe.2014.11.331
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Designing neural networks for modeling biological data: A statistical perspective

  • 1.
    Department of Economics and Statistics - University of Salerno, Via Giovanni Paolo II, 132. 84084 Fisciano (SA)
  • Received: 01 October 2012 Accepted: 29 June 2018 Published: 01 October 2013

  • MSC : Primary: 62G08, 62H15; Secondary: 62F40, 92B20.

  • 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.

    Citation: Michele La Rocca, Cira Perna. Designing neural networks for modeling biological data: A statistical perspective[J]. Mathematical Biosciences and Engineering, 2014, 11(2): 331-342. doi: 10.3934/mbe.2014.11.331

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  • 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.


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