On Sensitivity of Metalearning: An Illustrative Study for Robust Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00504176" target="_blank" >RIV/67985807:_____/20:00504176 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-57306-5_24" target="_blank" >http://dx.doi.org/10.1007/978-3-030-57306-5_24</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-57306-5_24" target="_blank" >10.1007/978-3-030-57306-5_24</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Sensitivity of Metalearning: An Illustrative Study for Robust Regression

Popis výsledku v původním jazyce

Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training datasets to a new (independent) dataset. While the concept of metalearning is becoming popular in statistical learning and readily available also for the analysis of economic datasets, not much attention has been paid to its limitations and disadvantages. To the best of our knowledge, the current paper represents a first illustration of metalearning sensitivity to data contamination by noise or outliers. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 24 datasets with economic background and perform a metalearning study over them as well as over the same datasets after an artificial contamination. The results reveal the whole process to remain rather sensitive to data contamination and some of the standard classifiers turn out to yield unreliable results. Nevertheless, using a robust classification method does not bring a desirable improvement. Thus, we conclude that the task of robustification of the whole metalearning methodology is more complex and deserves a systematic future research.

Název v anglickém jazyce

On Sensitivity of Metalearning: An Illustrative Study for Robust Regression

Popis výsledku anglicky

Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training datasets to a new (independent) dataset. While the concept of metalearning is becoming popular in statistical learning and readily available also for the analysis of economic datasets, not much attention has been paid to its limitations and disadvantages. To the best of our knowledge, the current paper represents a first illustration of metalearning sensitivity to data contamination by noise or outliers. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 24 datasets with economic background and perform a metalearning study over them as well as over the same datasets after an artificial contamination. The results reveal the whole process to remain rather sensitive to data contamination and some of the standard classifiers turn out to yield unreliable results. Nevertheless, using a robust classification method does not bring a desirable improvement. Thus, we conclude that the task of robustification of the whole metalearning methodology is more complex and deserves a systematic future research.

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

<a href="/cs/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytické základy neurovýpočtů</a><br>

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

Nonparametric Statistics. ISNPS 2018 Conference Proceedings

ISBN

978-3-030-57305-8

ISSN

2194-1009

e-ISSN

—

Počet stran výsledku

10

Strana od-do

261-270

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Salerno

Datum konání akce

11. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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