Learning when negative examples abound

Miroslav Kubat, Robert Holte, Stan Matwin

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

146 Scopus citations

Abstract

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
Pages146-153
Number of pages8
ISBN (Print)3540628584, 9783540628583
DOIs
StatePublished - 1997
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)
Volume1224
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th European Conference on Machine Learning, ECML 1997
CountryCzech Republic
CityPrague
Period4/23/974/25/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. In M. van Someren, G. Widmer, & G. Widmer (Eds.), Machine Learning: ECML-97 - 9th European Conference on Machine Learning, Proceedings (pp. 146-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224). Springer Verlag. https://doi.org/10.1007/3-540-62858-4_79