Abstract
Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.
| Original language | English |
|---|---|
| Title of host publication | Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings |
| Editors | Zbigniew W. Ras, Einoshin Suzuki, Ning Zhong, Shusaku Tsumoto |
| Publisher | Springer Verlag |
| Pages | 521-530 |
| Number of pages | 10 |
| ISBN (Print) | 3540202560, 9783540202561 |
| Publication status | Published - 2003 Jan 1 |
| Event | 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan Duration: 2003 Oct 28 → 2003 Oct 31 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 2871 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 |
|---|---|
| Country | Japan |
| City | Maebashi City |
| Period | 03/10/28 → 03/10/31 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. / Park, Chanho; Cho, Sung-Bae.
Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings. ed. / Zbigniew W. Ras; Einoshin Suzuki; Ning Zhong; Shusaku Tsumoto. Springer Verlag, 2003. p. 521-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2871).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification
AU - Park, Chanho
AU - Cho, Sung-Bae
PY - 2003/1/1
Y1 - 2003/1/1
N2 - Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.
AB - Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.
UR - http://www.scopus.com/inward/record.url?scp=0142245479&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0142245479&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0142245479
SN - 3540202560
SN - 9783540202561
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 521
EP - 530
BT - Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
A2 - Ras, Zbigniew W.
A2 - Suzuki, Einoshin
A2 - Zhong, Ning
A2 - Tsumoto, Shusaku
PB - Springer Verlag
ER -