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Article


Title

What makes multi-class imbalanced problems difficult? An experimental study

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Expert Systems with Applications

Journal year: 2022 | Journal volume: vol. 199

Article type

scientific article

Publication language

english

Keywords
EN
  • Imbalanced data
  • Classification
  • Learning from multiple classes
  • Data difficulty factors
Abstract

EN Multi-class imbalanced classification is more difficult and less frequently studied than its binary counterpart. Moreover, research on the causes of the difficulty of multi-class imbalanced data is quite limited and insufficient. Therefore, we experimentally study the impact of various multi-class imbalanced difficulty factors on the performance of three popular classifiers. The results demonstrated a strong influence of the class overlapping with the extent of its impact related to the types of overlapped classes. In particular, overlapping between minority and majority classes was more difficult than the others. The type of the class size configuration turned out to be another important factor, highlighting the special role of the configurations with classes of intermediate sizes. The obtained results could support studying the nature of the multi-class imbalanced data as well as the development of new methods for improving classifiers.

Date of online publication

02.04.2022

Pages (from - to)

116962-1 - 116962-13

DOI

10.1016/j.eswa.2022.116962

URL

https://www.sciencedirect.com/science/article/pii/S0957417422003888?via%3Dihub

Comments

Article Number: 116962

Points of MNiSW / journal

140.0

Impact Factor

6.954 [List 2020]

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