A Comparison of Robust Model Choice Criteria Within a Metalearning Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00531580" target="_blank" >RIV/67985807:_____/20:00531580 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-48814-7_7" target="_blank" >http://dx.doi.org/10.1007/978-3-030-48814-7_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-48814-7_7" target="_blank" >10.1007/978-3-030-48814-7_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparison of Robust Model Choice Criteria Within a Metalearning Study

Popis výsledku v původním jazyce

The methodology of automatic method selection (metalearning) allows to recommend the most suitable method (e.g. algorithm or statistical estimator) from several alternatives for a given dataset, based on information learned over a training database of datasets. Practitioners have become accustomed to using metalearning in the context of regression modeling, which is useful in a variety of applications in different fields. Still, none of previous metalearning studies on regression targeted at regression complexity issues and the majority of available metalearning studies for regression considered the standard mean square error as the prediction error measure. In this paper, a metalearning study focused on comparing different method selection criteria for the regression task is presented. A prediction rule, recommending the best regression estimator (possibly robust), is constructed over 31 training datasets. These are publicly available datasets, in which the linear model was carefully examined to be suitable. The results with the highest classification accuracy are obtained if the choice of the best estimator is based on robust versions of Akaike information criterion, particularly the version derived from MM-estimators. The work also advocates an implicitly weighted robust prediction mean square error.

Název v anglickém jazyce

A Comparison of Robust Model Choice Criteria Within a Metalearning Study

Popis výsledku anglicky

The methodology of automatic method selection (metalearning) allows to recommend the most suitable method (e.g. algorithm or statistical estimator) from several alternatives for a given dataset, based on information learned over a training database of datasets. Practitioners have become accustomed to using metalearning in the context of regression modeling, which is useful in a variety of applications in different fields. Still, none of previous metalearning studies on regression targeted at regression complexity issues and the majority of available metalearning studies for regression considered the standard mean square error as the prediction error measure. In this paper, a metalearning study focused on comparing different method selection criteria for the regression task is presented. A prediction rule, recommending the best regression estimator (possibly robust), is constructed over 31 training datasets. These are publicly available datasets, in which the linear model was carefully examined to be suitable. The results with the highest classification accuracy are obtained if the choice of the best estimator is based on robust versions of Akaike information criterion, particularly the version derived from MM-estimators. The work also advocates an implicitly weighted robust prediction mean square error.

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

17

Strana od-do

125-141

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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