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
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
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
T2 - 2019 International Electronics Communication Conference, IECC 2019
Y2 - 7 July 2019 through 9 July 2019
ER -