1997 | ReviewPaper | Buchkapitel
Learning when negative examples abound
verfasst von : Miroslav Kubat, Robert Holte, Stan Matwin
Erschienen in: Machine Learning: ECML-97
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.