Influence of resampling on accuracy of imbalanced classification

E. Burnaev, P. Erofeev, A. Papanov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

47 Citations (Scopus)

Abstract

In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is not much information about the minor class. One approach to deal with this problem is to preliminarily resample the dataset, i.e., add new elements to the dataset or remove existing ones. Resampling can be done in various ways which raises the problem of choosing the most appropriate one. In this paper we experimentally investigate impact of resampling on classification accuracy, compare resampling methods and highlight key points and difficulties of resampling.

Original languageEnglish
Title of host publicationEighth International Conference on Machine Vision, ICMV 2015
EditorsAntanas Verikas, Petia Radeva, Dmitry Nikolaev
PublisherSPIE
Volume9875
ISBN (Electronic)9781510601161
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Machine Vision, ICMV 2015 - Barcelona, Spain
Duration: 19 Nov 201521 Nov 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9875
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference8th International Conference on Machine Vision, ICMV 2015
Country/TerritorySpain
CityBarcelona
Period19/11/1521/11/15

Keywords

  • binary classification
  • Bootstrap oversampling
  • class imbalance
  • resampling
  • RUS
  • SMOTE

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