Learning when negative examples abound

Miroslav Kubat, Robert Holte, Stan Matwin

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

139 Citations (Scopus)

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages146-153
Number of pages8
Volume1224
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)
Volume1224
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Concept Learning
Learning Systems
Learning algorithms
Learning systems
Learning Algorithm
Attribute
Binary
Experiment
Experiments
Class
Learning
Training

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1224, pp. 146-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224). Springer Verlag.

Learning when negative examples abound. / Kubat, Miroslav; Holte, Robert; Matwin, Stan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1224 Springer Verlag, 1997. p. 146-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224).

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

Kubat, M, Holte, R & Matwin, S 1997, Learning when negative examples abound. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1224, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1224, Springer Verlag, pp. 146-153, 9th European Conference on Machine Learning, ECML 1997, Prague, Czech Republic, 4/23/97.
Kubat M, Holte R, Matwin S. Learning when negative examples abound. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1224. Springer Verlag. 1997. p. 146-153. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kubat, Miroslav ; Holte, Robert ; Matwin, Stan. / Learning when negative examples abound. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1224 Springer Verlag, 1997. pp. 146-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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