EVOLUTIONARY SUPPORT VECTOR MACHINE FOR PARAMETERS OPTIMIZATION APPLIED TO MEDICAL DIAGNOSTIC

Ahmed Kharrat, Nacéra Benamrane, Mohamed Ben Messaoud, Mohamed Abid

2011

Abstract

The parameter selection is very important for successful modelling of input–output relationship in a function classification model. In this study, support vector machine (SVM) has been used as a function classification tool for accurate segregation and genetic algorithm (GA) has been utilised for optimisation of the parameters of the SVM model. Having as input only five selected features, parameters optimisation for SVM is applied. The five selected features are mean of contrast, mean of homogeneity, mean of sum average, mean of sum variance and range of autocorrelation. The performance of the proposed model has been compared with a statistical approach. Despite the fact that Grid algorithm has fewer processing time, it does not seem to be efficient. Testing results show that the proposed GA–SVM model outperforms the statistical approach in terms of accuracy and computational efficiency.

Paper Citation

in Harvard Style

Kharrat A., Benamrane N., Ben Messaoud M. and Abid M. (2011). EVOLUTIONARY SUPPORT VECTOR MACHINE FOR PARAMETERS OPTIMIZATION APPLIED TO MEDICAL DIAGNOSTIC . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 201-204. DOI: 10.5220/0003326902010204

in Bibtex Style

@conference{visapp11,
author={Ahmed Kharrat and Nacéra Benamrane and Mohamed Ben Messaoud and Mohamed Abid},
title={EVOLUTIONARY SUPPORT VECTOR MACHINE FOR PARAMETERS OPTIMIZATION APPLIED TO MEDICAL DIAGNOSTIC},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={201-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003326902010204},
isbn={978-989-8425-47-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - EVOLUTIONARY SUPPORT VECTOR MACHINE FOR PARAMETERS OPTIMIZATION APPLIED TO MEDICAL DIAGNOSTIC
SN - 978-989-8425-47-8
AU - Kharrat A.
AU - Benamrane N.
AU - Ben Messaoud M.
AU - Abid M.
PY - 2011
SP - 201
EP - 204
DO - 10.5220/0003326902010204