How to determine the unique contributions of input-variables to the nonlinear regression function of a multilayer perceptron
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 el...
Gespeichert in:
| 1. Verfasser: | |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
15 May 2015
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| In: |
Ecological modelling
Year: 2015, Jahrgang: 309-310, Pages: 60-63 |
| ISSN: | 0304-3800 |
| DOI: | 10.1016/j.ecolmodel.2015.04.015 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.ecolmodel.2015.04.015 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0304380015001660 
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| Verfasserangaben: | Andreas Fischer |
MARC
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| 520 | |a 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. | ||
| 650 | 4 | |a Connection weight method | |
| 650 | 4 | |a Garson's method | |
| 650 | 4 | |a Method comparison | |
| 650 | 4 | |a Nonlinear regression | |
| 650 | 4 | |a Relative importance | |
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