Learning when negative examples abound
Lecture notes in computer sciencePublished 1 January 1997Open access
Miroslav Kubát, Robert C. Holte, Stan Matwin
Citations279
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper discusses one essential trouble brought about by imbalanced training sets and presents a learning algorithm addressing this issue.
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.
Keywords
Computer Science
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