The Construction of Support Vector Machine Classifier Using the Artificial Bee Colony Algorithm for Multi-Classifications of Ultrasonic Supraspinatus Images

DOI

10.2991/ceie-16.2017.71How to use a DOI?

Keywords

Support Vector Machines (SVM); Artificial Bee Colony Algorithm; Ultrasonic Supraspinatus Images; LIBSVM; Multi-Classifications

Abstract

The setting of parameters in the support vector machines (SVM) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter and Lagrangian multiplier. The proposed method is called the artificial bee colony-based SVM (ABC-SVM) classifier. The ABC-SVM classifier is not considered with the feature selection because the SVM together with feature selection is not suitable for being applied to a multi-class classification problem, especially for the one-against-all multi-class SVM. The experiments are on the multi-class diagnosis of ultrasonic supraspinatus images. The experimental results demonstrated that the use of ABC-SVM classifier in classifying the patterns has maximum accuracy compared with LIBSVM.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Minghuwi Horng
PY  - 2016/10
DA  - 2016/10
TI  - The Construction of Support Vector Machine Classifier Using the Artificial Bee Colony Algorithm for Multi-Classifications of Ultrasonic Supraspinatus Images
BT  - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
PB  - Atlantis Press
SP  - 554
EP  - 560
SN  - 2352-5401
UR  - https://doi.org/10.2991/ceie-16.2017.71
DO  - 10.2991/ceie-16.2017.71
ID  - Horng2016/10
ER  -