A Metalearning Study for Robust Nonlinear Regression

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

A Metalearning Study for Robust Nonlinear Regression

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Article name in the collection

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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Number of pages

12

Pages from-to

499-510

Publisher name

Springer

Place of publication

Cham

Event location

Halkidiki

Event date

Jun 5, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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