Learning when negative examples abound View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1997

AUTHORS

Miroslav Kubat , Robert Holte , Stan Matwin

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. More... »

PAGES

146-153

Book

TITLE

Machine Learning: ECML-97

ISBN

978-3-540-62858-3
978-3-540-68708-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-62858-4_79

DOI

http://dx.doi.org/10.1007/3-540-62858-4_79

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015106998


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