On Usefulness of Outlier Elimination in Classification Tasks

HETLEROVIĆ, Dušan, Lubomír POPELÍNSKÝ, P. BRAZDIL, C. SOARES a F. FREAITAS. On Usefulness of Outlier Elimination in Classification Tasks. Online. In Tassadit Bouadi, Élisa Fromont, Eyke Hüllermeier. International Symposium on Intelligent Data Analysis 2022. Rennes: Springer, 2022, s. 143-156. ISBN 978-3-031-01332-4. Dostupné z: https://dx.doi.org/10.1007/978-3-031-01333-1_12.

Základní údaje

Originální název

On Usefulness of Outlier Elimination in Classification Tasks

Název česky

On Usefulness of Outlier Elimination in Classification Tasks

Autoři

HETLEROVIĆ, Dušan (703 Slovensko, domácí), Lubomír POPELÍNSKÝ (203 Česká republika, domácí), P. BRAZDIL, C. SOARES a F. FREAITAS

Vydání

Rennes, International Symposium on Intelligent Data Analysis 2022, od s. 143-156, 14 s. 2022

Nakladatel

Springer

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/22:00126186

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-01332-4

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-01333-1_12

UT WoS

000937256100012

Klíčová slova česky

Outlier elimination; Metalearning; Average ranking; Reduction of portfolios

Klíčová slova anglicky

Outlier elimination; Metalearning; Average ranking; Reduction of portfolios

Štítky

core_B, firank_A

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

V originále

Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our study is to fill in this gap. We have performed experiments with 12 various outlier elimination methods and 10 classification algorithms on 50 different datasets. The results were then processed by the proposed reduction method, whose aim is identify the most useful workflows for a given set of tasks (datasets). The reduction method has identified that just three OEMs that are generally useful for the given set of tasks. We have shown that the inclusion of these OEMs is indeed useful, as it leads to lower loss in accuracy and the difference is quite significant (0.5\%) on average.

Česky

Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our study is to fill in this gap. We have performed experiments with 12 various outlier elimination methods and 10 classification algorithms on 50 different datasets. The results were then processed by the proposed reduction method, whose aim is identify the most useful workflows for a given set of tasks (datasets). The reduction method has identified that just three OEMs that are generally useful for the given set of tasks. We have shown that the inclusion of these OEMs is indeed useful, as it leads to lower loss in accuracy and the difference is quite significant (0.5\%) on average.