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

Další údaje

• Originální 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: 0302-9743
• 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: firank_A
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.

Anotace 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.