Abstract
The one-classification support vector (OCSVM) is a variant of SVM which only uses the positive class sample set in training stage. It has been widely used in the applications of disease diagnose, handwritten signature verification, remote sensing and document classification. However, there are many parameters needed to regulate. The mistake of parameter setting makes OCSVM it to be not effectiveness. Therefore, in this paper we proposed a learning algorithm based on the artificial bee colony algorithm to select the parameters. The construction algorithm of OSCVM is called the artificial bee colony based OSCVM (ABC-OCSVM) algorithm. Experimental results of two medical datasets of UCI data repository showed that our proposed ABC-OCSVM method outperforms the conventional LIBSVM package.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2019 International Electronics Communication Conference, IECC 2019 |
| Publisher | Association for Computing Machinery |
| Pages | 71-75 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781450371773 |
| DOIs | |
| Publication status | Published - 2019 Jul 7 |
| Event | 2019 International Electronics Communication Conference, IECC 2019 - Okinawa, Japan Duration: 2019 Jul 7 → 2019 Jul 9 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | 2019 International Electronics Communication Conference, IECC 2019 |
|---|---|
| Country | Japan |
| City | Okinawa |
| Period | 19-07-07 → 19-07-09 |
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All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
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Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification. / Horng, Ming Huwi; Hong, Yu Lun; Sun, Yung-Nien; Zhan, Zhe Yuan; Hong, Chen Yu.
Proceedings of the 2019 International Electronics Communication Conference, IECC 2019. Association for Computing Machinery, 2019. p. 71-75 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification
AU - Horng, Ming Huwi
AU - Hong, Yu Lun
AU - Sun, Yung-Nien
AU - Zhan, Zhe Yuan
AU - Hong, Chen Yu
PY - 2019/7/7
Y1 - 2019/7/7
N2 - The one-classification support vector (OCSVM) is a variant of SVM which only uses the positive class sample set in training stage. It has been widely used in the applications of disease diagnose, handwritten signature verification, remote sensing and document classification. However, there are many parameters needed to regulate. The mistake of parameter setting makes OCSVM it to be not effectiveness. Therefore, in this paper we proposed a learning algorithm based on the artificial bee colony algorithm to select the parameters. The construction algorithm of OSCVM is called the artificial bee colony based OSCVM (ABC-OCSVM) algorithm. Experimental results of two medical datasets of UCI data repository showed that our proposed ABC-OCSVM method outperforms the conventional LIBSVM package.
AB - The one-classification support vector (OCSVM) is a variant of SVM which only uses the positive class sample set in training stage. It has been widely used in the applications of disease diagnose, handwritten signature verification, remote sensing and document classification. However, there are many parameters needed to regulate. The mistake of parameter setting makes OCSVM it to be not effectiveness. Therefore, in this paper we proposed a learning algorithm based on the artificial bee colony algorithm to select the parameters. The construction algorithm of OSCVM is called the artificial bee colony based OSCVM (ABC-OCSVM) algorithm. Experimental results of two medical datasets of UCI data repository showed that our proposed ABC-OCSVM method outperforms the conventional LIBSVM package.
UR - http://www.scopus.com/inward/record.url?scp=85072027741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072027741&partnerID=8YFLogxK
U2 - 10.1145/3343147.3343152
DO - 10.1145/3343147.3343152
M3 - Conference contribution
AN - SCOPUS:85072027741
T3 - ACM International Conference Proceeding Series
SP - 71
EP - 75
BT - Proceedings of the 2019 International Electronics Communication Conference, IECC 2019
PB - Association for Computing Machinery
ER -