Regression Neural Networks with a Highly Robust Loss Function

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00522365" target="_blank" >RIV/67985807:_____/20:00522365 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-48814-7_2" target="_blank" >http://dx.doi.org/10.1007/978-3-030-48814-7_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-48814-7_2" target="_blank" >10.1007/978-3-030-48814-7_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regression Neural Networks with a Highly Robust Loss Function

Popis výsledku v původním jazyce

Artificial neural networks represent an important class of methods for fitting nonlinear regression to data with an unknown regression function. However, usual ways of training of the most common types of neural networks applied to nonlinear regression tasks suffer from the presence of outlying measurements (outliers) in the data. So far, only a few robust alternatives for training common forms of neural networks have been proposed. In this work, we robustify two common types of neural networks by considering robust versions of their loss functions, which have turned out to be successful in linear regression. Particularly, we extend the idea of using the loss of the least trimmed squares estimator to radial basis function networks. We also propose multilayer perceptrons and radial basis function networks based on the loss of the least weighted squares estimator. The performance of these novel methods is compared with that of standard neural networks on 4 datasets. The results bring arguments in favor of the novel robust approach based on the least weighted squares estimator with trimmed linear weights in terms of yielding the smallest robust prediction error in a variety of situations. Robust neural networks are even able to outperform the prediction ability of support vector regression.

Název v anglickém jazyce

Regression Neural Networks with a Highly Robust Loss Function

Popis výsledku anglicky

Artificial neural networks represent an important class of methods for fitting nonlinear regression to data with an unknown regression function. However, usual ways of training of the most common types of neural networks applied to nonlinear regression tasks suffer from the presence of outlying measurements (outliers) in the data. So far, only a few robust alternatives for training common forms of neural networks have been proposed. In this work, we robustify two common types of neural networks by considering robust versions of their loss functions, which have turned out to be successful in linear regression. Particularly, we extend the idea of using the loss of the least trimmed squares estimator to radial basis function networks. We also propose multilayer perceptrons and radial basis function networks based on the loss of the least weighted squares estimator. The performance of these novel methods is compared with that of standard neural networks on 4 datasets. The results bring arguments in favor of the novel robust approach based on the least weighted squares estimator with trimmed linear weights in terms of yielding the smallest robust prediction error in a variety of situations. Robust neural networks are even able to outperform the prediction ability of support vector regression.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Analytical Methods in Statistics

ISBN

978-3-030-48813-0

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

17-29

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Liberec

Datum konání akce

16. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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