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How to determine the unique contributions of input-variables to the nonlinear regression function of a multilayer perceptron

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  • Fischer, Andreas

Abstract

There are different methods for quantifying the relative contribution of input-variables to the nonlinear regression function provided by a Multilayer Perceptron. Unfortunately most of the systematic method comparisons available to date suffer from a set of characteristic shortcomings. This paper elaborates on these methodological shortcomings and presents a simulation study that demonstrates how to avoid them in future method comparisons. Results of the simulation study indicate that Garson's weight method is preferable to the connection weight method proposed by Olden et al., (2004) for each of the samples simulated.

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  • Fischer, Andreas, 2015. "How to determine the unique contributions of input-variables to the nonlinear regression function of a multilayer perceptron," Ecological Modelling, Elsevier, vol. 309, pages 60-63.
    • Handle: RePEc:eee:ecomod:v:309-310:y:2015:i::p:60-63
    DOI: 10.1016/j.ecolmodel.2015.04.015
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    References listed on IDEAS

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    1. Kemp, Stanley J. & Zaradic, Patricia & Hansen, Frank, 2007. "An approach for determining relative input parameter importance and significance in artificial neural networks," Ecological Modelling, Elsevier, vol. 204(3), pages 326-334.
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    1. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Shi Sun & Cheng Sun & Dorine C. Duives & Serge P. Hoogendoorn, 2023. "Neural network model for predicting variation in walking dynamics of pedestrians in social groups," Transportation, Springer, vol. 50(3), pages 837-868, June.
    3. Cheng, Jin & Wang, Jian & Wu, Xuezhou & Wang, Shuo, 2019. "An improved polynomial-based nonlinear variable importance measure and its application to degradation assessment for high-voltage transformer under imbalance data," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 175-191.
    4. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.

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