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 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 146-153 |
| Number of pages | 8 |
| Volume | 1224 |
| ISBN (Print) | 3540628584, 9783540628583 |
| State | Published - 1997 |
| Externally published | Yes |
| 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) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 9th European Conference on Machine Learning, ECML 1997 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 4/23/97 → 4/25/97 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Learning when negative examples abound. / Kubat, Miroslav; Holte, Robert; Matwin, Stan.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1224 Springer Verlag, 1997. p. 146-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1224).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning when negative examples abound
AU - Kubat, Miroslav
AU - Holte, Robert
AU - Matwin, Stan
PY - 1997
Y1 - 1997
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84947918649&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84947918649&partnerID=8YFLogxK
M3 - Conference contribution
SN - 3540628584
SN - 9783540628583
VL - 1224
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 146
EP - 153
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -