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.
Cite
CITATION STYLE
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). Springer Verlag. https://doi.org/10.1007/3-540-62858-4_79
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