A Metalearning Study for Robust Nonlinear Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00524791" target="_blank" >RIV/67985807:_____/20:00524791 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-48791-1_39" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-48791-1_39</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-48791-1_39" target="_blank" >10.1007/978-3-030-48791-1_39</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Metalearning Study for Robust Nonlinear Regression

Popis výsledku v původním jazyce

Metalearning is a methodology aiming at recommending the most suitable algorithm (or method) from several alternatives for a particular dataset. Its classification rule is learned over an available training database of datasets. It gradually penetrates to various applications in computer science and has also the potential to recommend the most suitable statistical estimator for a given dataset. We consider the nonlinear regression model. While there are some robust alternatives to the traditional (and very non-robust) nonlinear least squares available, it is not theoretically known which estimator performs the best for a particular dataset. In this work, we perform a metalearning study performed over 721 datasets predicting the best nonlinear regression estimator for an independent dataset. The estimators considered here include standard nonlinear least squares as well as its robust alternatives with a high breakdown point. On the whole, the presented study brings new arguments in favor of the nonlinear least weighted squares estimator, which is based on the idea to assign implicit weights to individual observations based on outlyingness of their residuals.

Název v anglickém jazyce

A Metalearning Study for Robust Nonlinear Regression

Popis výsledku anglicky

Metalearning is a methodology aiming at recommending the most suitable algorithm (or method) from several alternatives for a particular dataset. Its classification rule is learned over an available training database of datasets. It gradually penetrates to various applications in computer science and has also the potential to recommend the most suitable statistical estimator for a given dataset. We consider the nonlinear regression model. While there are some robust alternatives to the traditional (and very non-robust) nonlinear least squares available, it is not theoretically known which estimator performs the best for a particular dataset. In this work, we perform a metalearning study performed over 721 datasets predicting the best nonlinear regression estimator for an independent dataset. The estimators considered here include standard nonlinear least squares as well as its robust alternatives with a high breakdown point. On the whole, the presented study brings new arguments in favor of the nonlinear least weighted squares estimator, which is based on the idea to assign implicit weights to individual observations based on outlyingness of their residuals.

Druh

D - Stať ve sborníku

CEP obor

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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

Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

ISBN

978-3-030-48790-4

ISSN

2661-8141

e-ISSN

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Počet stran výsledku

12

Strana od-do

499-510

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Halkidiki

Datum konání akce

5. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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