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.
| Original language | English (US) |
|---|---|
| Title of host publication | Machine Learning |
| Subtitle of host publication | ECML-97 - 9th European Conference on Machine Learning, Proceedings |
| Editors | Maarten van Someren, Gerhard Widmer, Gerhard Widmer |
| Publisher | Springer Verlag |
| Pages | 146-153 |
| Number of pages | 8 |
| ISBN (Print) | 3540628584, 9783540628583 |
| DOIs | |
| State | Published - 1997 |
| Event | 9th European Conference on Machine Learning, ECML 1997 - Prague, Czech Republic Duration: Apr 23 1997 → Apr 25 1997 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 1224 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 9th European Conference on Machine Learning, ECML 1997 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 4/23/97 → 4/25/97 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Fingerprint Dive into the research topics of 'Learning when negative examples abound'. Together they form a unique fingerprint.
Cite this
Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. In M. van Someren, G. Widmer, & G. Widmer (Eds.), Machine Learning: ECML-97 - 9th European Conference on Machine Learning, Proceedings (pp. 146-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224). Springer Verlag. https://doi.org/10.1007/3-540-62858-4_79