Evaluation of sampling methods for learning from imbalanced data

Garima Goel, Liam Maguire, Yuhua Li, Sean McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

The problem of learning from imbalanced data is of critical importance in a large number of application domains and can be a bottleneck in the performance of various conventional learning methods that assume the data distribution to be balanced. The class imbalance problem corresponds to dealing with the situation where one class massively outnumbers the other. The imbalance between majority and minority would lead machine learning to be biased and produce unreliable outcomes if the imbalanced data is used directly. There has been increasing interest in this research area and a number of algorithms have been developed. However, independent evaluation of the algorithms is limited. This paper aims at evaluating the performance of five representative data sampling methods namely SMOTE, ADASYN, BorderlineSMOTE, SMOTETomek and RUSBoost that deal with class imbalance problems. A comparative study is conducted and the performance of each method is critically analysed in terms of assessment metrics.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages392-401
Number of pages10
Volume7995 LNCS
DOIs
Publication statusPublished - 28 Aug 2013
Event9th International Conference on Intelligent Computing, ICIC 2013 - Nanning, China
Duration: 28 Jul 201331 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7995 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference9th International Conference on Intelligent Computing, ICIC 2013
CountryChina
CityNanning
Period28/07/201331/07/2013

Keywords

  • imbalanced data
  • sampling methods
  • support vector machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Evaluation of sampling methods for learning from imbalanced data'. Together they form a unique fingerprint.

  • Cite this

    Goel, G., Maguire, L., Li, Y., & McLoone, S. (2013). Evaluation of sampling methods for learning from imbalanced data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7995 LNCS, pp. 392-401). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7995 LNCS). https://doi.org/10.1007/978-3-642-39479-9_47