Detailed Information on Publication Record
2022
On Usefulness of Outlier Elimination in Classification Tasks
HETLEROVIĆ, Dušan, Lubomír POPELÍNSKÝ, P. BRAZDIL, C. SOARES, F. FREAITAS et. al.Basic information
Original name
On Usefulness of Outlier Elimination in Classification Tasks
Name in Czech
On Usefulness of Outlier Elimination in Classification Tasks
Authors
HETLEROVIĆ, Dušan (703 Slovakia, belonging to the institution), Lubomír POPELÍNSKÝ (203 Czech Republic, belonging to the institution), P. BRAZDIL, C. SOARES and F. FREAITAS
Edition
Rennes, International Symposium on Intelligent Data Analysis 2022, p. 143-156, 14 pp. 2022
Publisher
Springer
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
Impact factor
Impact factor: 0.402 in 2005
RIV identification code
RIV/00216224:14330/22:00126186
Organization unit
Faculty of Informatics
ISBN
978-3-031-01332-4
ISSN
UT WoS
000937256100012
Keywords (in Czech)
Outlier elimination; Metalearning; Average ranking; Reduction of portfolios
Keywords in English
Outlier elimination; Metalearning; Average ranking; Reduction of portfolios
Tags
International impact, Reviewed
Změněno: 28/3/2023 12:48, RNDr. Pavel Šmerk, Ph.D.
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.
In Czech
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.