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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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