On Sensitivity of Metalearning: An Illustrative Study for Robust Regression
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
On Sensitivity of Metalearning: An Illustrative Study for Robust Regression
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytical Foundations of Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Nonparametric Statistics. ISNPS 2018 Conference Proceedings
ISBN
978-3-030-57305-8
ISSN
2194-1009
e-ISSN
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Number of pages
10
Pages from-to
261-270
Publisher name
Springer
Place of publication
Cham
Event location
Salerno
Event date
Jun 11, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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