@inproceedings{71c6c139d3b0471f9ffcb358a4b03dee,
title = "Learning when negative examples abound",
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.",
author = "Miroslav Kubat and Robert Holte and Stan Matwin",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 9th European Conference on Machine Learning, ECML 1997 ; Conference date: 23-04-1997 Through 25-04-1997",
year = "1997",
doi = "10.1007/3-540-62858-4_79",
language = "English (US)",
isbn = "3540628584",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "146--153",
editor = "{van Someren}, Maarten and Gerhard Widmer and Gerhard Widmer",
booktitle = "Machine Learning",
}