Název: Robust Metalearning: Comparing Robust Regression Using A Robust Prediction Error
Autoři: Peštová, Barbora ; Kalina, Jan
Typ dokumentu: Příspěvky z konference
Konference/Akce: International Days of Statistics and Economics /12./, Prague (CZ), 20180906
Rok: 2018
Jazyk: eng
Abstrakt: The aim of this paper is to construct a classification rule for predicting the best regression estimator for a new data set based on a database of 20 training data sets. Various estimators considered here include some popular methods of robust statistics. The methodology used for constructing the classification rule can be described as metalearning. Nevertheless, standard approaches of metalearning should be robustified if working with data sets contaminated by outlying measurements (outliers). Therefore, our contribution can be also described as robustification of the metalearning process by using a robust prediction error. In addition to performing the metalearning study by means of both standard and robust approaches, we search for a detailed interpretation in two particular situations. The results of detailed investigation show that the knowledge obtained by a metalearning approach standing on standard principles is prone to great variability and instability, which makes it hard to believe that the results are not just a consequence of a mere chance. Such aspect of metalearning seems not to have been previously analyzed in literature.
Klíčová slova: metalearning; outliers; robust prediction error; robust regression
Číslo projektu: GA17-01251S (CEP)
Poskytovatel projektu: GA ČR
Zdrojový dokument: The 12th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-14-8

Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: https://msed.vse.cz/msed_2018/article/13-Pestova-Barbora-paper.pdf
Původní záznam: http://hdl.handle.net/11104/0289877

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-390096


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