Publication details

On Usefulness of Outlier Elimination in Classification Tasks



Year of publication 2022
Type Article in Proceedings
Conference International Symposium on Intelligent Data Analysis 2022
MU Faculty or unit

Faculty of Informatics

Keywords Outlier elimination; Metalearning; Average ranking; Reduction of portfolios
Description 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.

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