Grey relational analysis feature selection for cancer classification using support vector machine

Nowadays, cancer is one of the leading causes of death in the world. However, cancer can be treated if it is diagnosed earlier. Recently, machine learning classifiers are widely applied in cancer detection due to their accurate diagnosis in cancer classification problems. However, the performance of...

Full description

Saved in:

Bibliographic Details
Main Author:	Sy. Ahmad Ubaidillah, Sharifah Hafizah
Format:	Thesis
Published:	2014
Subjects:	TJ Mechanical engineering and machinery
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.48461
record_format	uketd_dc
spelling	my-utm-ep.484612017-08-09T07:56:12Z Grey relational analysis feature selection for cancer classification using support vector machine 2014 Sy. Ahmad Ubaidillah, Sharifah Hafizah TJ Mechanical engineering and machinery Nowadays, cancer is one of the leading causes of death in the world. However, cancer can be treated if it is diagnosed earlier. Recently, machine learning classifiers are widely applied in cancer detection due to their accurate diagnosis in cancer classification problems. However, the performance of the classifiers can be affected by the selection of the required variables used in the classification process. To choose these variables, this research proposed two classification models using two different feature selection methods namely: Grey Relational Analysis (GRA) and Improved Grey Relational Analysis (IGRA). Both of these methods are combined with a Support Vector Machine (SVM) classifier and named as GRA-SVM and IGRA-SVM. The GRA and IGRA act as a feature selection method in the preprocessing phase of SVM classifier to recognize potential variables in cancer data that can be used as significant input to SVM classifier to improve SVM classification capability performance. Using performance measuring tools, the efficiency of the proposed classification models: GRA-SVM and IGRA-SVM based on the value of geometric mean, sensitivity, specificity, accuracy and area under Receiver Operating Characteristic curve were compared with standard SVM and other classification models from previous studies. The results showed that the proposed GRA-SVM and IGRA-SVM classification models have achieved better performance in classifying the cancer data with better results ranging between 2.64% to 88.9% in the selection of potential variables 2014 Thesis http://eprints.utm.my/id/eprint/48461/ masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Sy. Ahmad Ubaidillah, Sharifah Hafizah Grey relational analysis feature selection for cancer classification using support vector machine
description	Nowadays, cancer is one of the leading causes of death in the world. However, cancer can be treated if it is diagnosed earlier. Recently, machine learning classifiers are widely applied in cancer detection due to their accurate diagnosis in cancer classification problems. However, the performance of the classifiers can be affected by the selection of the required variables used in the classification process. To choose these variables, this research proposed two classification models using two different feature selection methods namely: Grey Relational Analysis (GRA) and Improved Grey Relational Analysis (IGRA). Both of these methods are combined with a Support Vector Machine (SVM) classifier and named as GRA-SVM and IGRA-SVM. The GRA and IGRA act as a feature selection method in the preprocessing phase of SVM classifier to recognize potential variables in cancer data that can be used as significant input to SVM classifier to improve SVM classification capability performance. Using performance measuring tools, the efficiency of the proposed classification models: GRA-SVM and IGRA-SVM based on the value of geometric mean, sensitivity, specificity, accuracy and area under Receiver Operating Characteristic curve were compared with standard SVM and other classification models from previous studies. The results showed that the proposed GRA-SVM and IGRA-SVM classification models have achieved better performance in classifying the cancer data with better results ranging between 2.64% to 88.9% in the selection of potential variables
format	Thesis
qualification_level	Master's degree
author	Sy. Ahmad Ubaidillah, Sharifah Hafizah
author_facet	Sy. Ahmad Ubaidillah, Sharifah Hafizah
author_sort	Sy. Ahmad Ubaidillah, Sharifah Hafizah
title	Grey relational analysis feature selection for cancer classification using support vector machine
title_short	Grey relational analysis feature selection for cancer classification using support vector machine
title_full	Grey relational analysis feature selection for cancer classification using support vector machine
title_fullStr	Grey relational analysis feature selection for cancer classification using support vector machine
title_full_unstemmed	Grey relational analysis feature selection for cancer classification using support vector machine
title_sort	grey relational analysis feature selection for cancer classification using support vector machine
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
_version_	1747817395964084224

Grey relational analysis feature selection for cancer classification using support vector machine

Similar Items