Using information on class interrelations to improve classification of multiclass imbalanced data: A new resampling algorithm

• Autorzy: Janicka Małgorzata , Lango Mateusz , Stefanowski Jerzy

Identyfikatory

DOI

10.2478/amcs-2019-0057

• Języki publikacji: EN

Abstrakty

EN

The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.

Słowa kluczowe

EN

imbalanced data; multi-class learning; re-sampling; data difficulty factor; similarity degree

PL

dane niezrównoważone; ponowne próbkowanie; stopień podobieństwa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2019
• Tom: Vol. 29, no. 4
• Strony: 769--781
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Janicka Małgorzata

• Institute of Computing Sciences, Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland

autor

Lango Mateusz

• Institute of Computing Sciences, Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland

autor

Stefanowski Jerzy

• Institute of Computing Sciences, Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznań, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).