Learning when negative examples abound

Miroslav Kubat, Robert Holte, Stan Matwin

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

167 Scopus citations


Existing concept learning systems can fail when the negative examples heavily outnumber the positive examples. The paper discusses one essential trouble brought about by imbalanced training sets and presents a learning algorithm addressing this issue. The experiments (with synthetic and real-world data) focus on 2-class problems with examples described with binary and continuous attributes.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML-97 - 9th European Conference on Machine Learning, Proceedings
EditorsMaarten van Someren, Gerhard Widmer, Gerhard Widmer
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540628584, 9783540628583
StatePublished - 1997
Externally publishedYes
Event9th European Conference on Machine Learning, ECML 1997 - Prague, Czech Republic
Duration: Apr 23 1997Apr 25 1997

Publication series

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


Other9th European Conference on Machine Learning, ECML 1997
Country/TerritoryCzech Republic

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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