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Original title:
Robust Metalearning: Comparing Robust Regression Using A Robust Prediction Error
Authors: Peštová, Barbora ; Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /12./, Prague (CZ), 20180906
Year: 2018
Language: eng
Abstract: 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.
Keywords: metalearning; outliers; robust prediction error; robust regression
Project no.: GA17-01251S (CEP)
Funding provider: GA ČR
Host item entry: The 12th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-14-8
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2018/article/13-Pestova-Barbora-paper.pdf
Original record: http://hdl.handle.net/11104/0289877
Permalink: http://www.nusl.cz/ntk/nusl-390096
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Peštová, Barbora ; Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /12./, Prague (CZ), 20180906
Year: 2018
Language: eng
Abstract: 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.
Keywords: metalearning; outliers; robust prediction error; robust regression
Project no.: GA17-01251S (CEP)
Funding provider: GA ČR
Host item entry: The 12th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-14-8
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2018/article/13-Pestova-Barbora-paper.pdf
Original record: http://hdl.handle.net/11104/0289877
Permalink: http://www.nusl.cz/ntk/nusl-390096
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2018-12-07, last modified 2024-11-10