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An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis

To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. T...

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Main Authors: Sallehuddin, R., Ahmad Ubaidillah, Sharifah Hafizah Sy., Zain, A. M., Alwee, R., Radzi, N. H. M.
Format: Article
Language:English
Published: Penerbit UTM Press 2016
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/71206/1/RoselinaSallehuddin2016_Animprovementinsupportvector.pdf
http://eprints.utm.my/id/eprint/71206/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988384738&doi=10.11113%2fjt.v78.9548&partnerID=40&md5=43bfb08fc623c9f663b8bf09a29613ee
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spelling my.utm.712062017-11-15T04:08:36Z http://eprints.utm.my/id/eprint/71206/ An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis Sallehuddin, R. Ahmad Ubaidillah, Sharifah Hafizah Sy. Zain, A. M. Alwee, R. Radzi, N. H. M. QA75 Electronic computers. Computer science To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48 by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97 by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/71206/1/RoselinaSallehuddin2016_Animprovementinsupportvector.pdf Sallehuddin, R. and Ahmad Ubaidillah, Sharifah Hafizah Sy. and Zain, A. M. and Alwee, R. and Radzi, N. H. M. (2016) An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis. Jurnal Teknologi, 78 (8-2). pp. 107-119. ISSN 0127-9696 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988384738&doi=10.11113%2fjt.v78.9548&partnerID=40&md5=43bfb08fc623c9f663b8bf09a29613ee
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sallehuddin, R.
Ahmad Ubaidillah, Sharifah Hafizah Sy.
Zain, A. M.
Alwee, R.
Radzi, N. H. M.
An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
description To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48 by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97 by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis.
format Article
author Sallehuddin, R.
Ahmad Ubaidillah, Sharifah Hafizah Sy.
Zain, A. M.
Alwee, R.
Radzi, N. H. M.
author_facet Sallehuddin, R.
Ahmad Ubaidillah, Sharifah Hafizah Sy.
Zain, A. M.
Alwee, R.
Radzi, N. H. M.
author_sort Sallehuddin, R.
title An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
title_short An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
title_full An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
title_fullStr An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
title_full_unstemmed An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
title_sort improvement in support vector machine classification model using grey relational analysis for cancer diagnosis
publisher Penerbit UTM Press
publishDate 2016
url http://eprints.utm.my/id/eprint/71206/1/RoselinaSallehuddin2016_Animprovementinsupportvector.pdf
http://eprints.utm.my/id/eprint/71206/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988384738&doi=10.11113%2fjt.v78.9548&partnerID=40&md5=43bfb08fc623c9f663b8bf09a29613ee
_version_ 1643656135486472192
score 13.211869

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