Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification

Ming Huwi Horng, Yu Lun Hong, Yung-Nien Sun, Zhe Yuan Zhan, Chen Yu Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the 2019 International Electronics Communication Conference, IECC 2019
PublisherAssociation for Computing Machinery
Pages71-75
Number of pages5
ISBN (Electronic)9781450371773
DOIs
Publication statusPublished - 2019 Jul 7
Event2019 International Electronics Communication Conference, IECC 2019 - Okinawa, Japan
Duration: 2019 Jul 72019 Jul 9

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 International Electronics Communication Conference, IECC 2019
CountryJapan
CityOkinawa
Period19-07-0719-07-09

Fingerprint

Support vector machines
Learning algorithms
Remote sensing

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Horng, M. H., Hong, Y. L., Sun, Y-N., Zhan, Z. Y., & Hong, C. Y. (2019). Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification. In Proceedings of the 2019 International Electronics Communication Conference, IECC 2019 (pp. 71-75). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3343147.3343152
Horng, Ming Huwi ; Hong, Yu Lun ; Sun, Yung-Nien ; Zhan, Zhe Yuan ; Hong, Chen Yu. / Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification. Proceedings of the 2019 International Electronics Communication Conference, IECC 2019. Association for Computing Machinery, 2019. pp. 71-75 (ACM International Conference Proceeding Series).
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Horng, MH, Hong, YL, Sun, Y-N, Zhan, ZY & Hong, CY 2019, Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification. in Proceedings of the 2019 International Electronics Communication Conference, IECC 2019. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 71-75, 2019 International Electronics Communication Conference, IECC 2019, Okinawa, Japan, 19-07-07. https://doi.org/10.1145/3343147.3343152

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 proceedingConference contribution

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Horng MH, Hong YL, Sun Y-N, Zhan ZY, Hong CY. Learning one-class support vector machine by using artificial bee colony algorithm and its application for disease classification. In Proceedings of the 2019 International Electronics Communication Conference, IECC 2019. Association for Computing Machinery. 2019. p. 71-75. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3343147.3343152