Empowering imbalanced data in supervised learning: A semi-supervised learning approach

Bassam A. Almogahed, Ioannis A. Kakadiaris

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

5 Scopus citations

Abstract

We present a framework to address the imbalanced data problem using semi-supervised learning. Specifically, from a supervised problem, we create a semi-supervised problem and then use a semi-supervised learning method to identify the most relevant instances to establish a well-defined training set. We present extensive experimental results, which demonstrate that the proposed framework significantly outperforms all other sampling algorithms in 67% of the cases across three different classifiers and ranks second best for the remaining 33% of the cases.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer-Verlag
Pages523-530
Number of pages8
ISBN (Print)9783319111780
DOIs
StatePublished - 2014
Event24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
Duration: Sep 15 2014Sep 19 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Artificial Neural Networks, ICANN 2014
Country/TerritoryGermany
CityHamburg
Period9/15/149/19/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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