Performance Analysis of SMOTE-Based Oversampling Techniques When Dealing with Data Imbalance (CROSBI ID 678812)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bajer, Dražen ; Zorić, Bruno ; Dudjak, Mario ; Martinović, Goran
engleski
Performance Analysis of SMOTE-Based Oversampling Techniques When Dealing with Data Imbalance
Building classification models on imbalanced data proves to be a challenging task despite the multitude of available classifiers. The classifier bias towards the majority class can be ameliorated through various manners and with varying degrees of success. Oversampling minority instances or undersampling majority ones are prominent amongst these due both to their simplicity and effectiveness. Probably the most popular approach to oversampling is the well-known SMOTE algorithm, based on which numerous enhancement attempts were made. This paper aims to compare the performance of these, more complex, oversampling techniques with regard to the original on a wide array of problems. Additionally, it attempts to give insight into the behaviour of different interpretations of the original algorithm apparent in the literature. In that regard, some interesting findings were made.
classification ; data imbalance ; minority oversampling ; SMOTE
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
265-271.
2019.
objavljeno
10.1109/IWSSIP.2019.8787306
Podaci o matičnoj publikaciji
Proceedings of IWSSIP 2019
Rimac-Drlje, Snježana ; Žagar, Drago ; Galić, Irena ; Martinović, Goran ; Vranješ, Denis ; Habijan, Marija
Osijek: Fakultet elektrotehnike, računarstva i informacijskih tehnologija Sveučilišta Josipa Jurja Strossmayera u Osijeku
978-1-7281-3253-2
Podaci o skupu
26th International Conference on Systems, Signals and Image Processing (IWSSIP 2019)
predavanje
05.06.2019-07.06.2019
Osijek, Hrvatska