Support vector machine for solving imbalanced dataset problem

Most of machine learning classifiers such as Neural Network (NN), Naïve Bayes, and Decision Tree Method C4.5 are failed to classify the data when it deals with imbalanced data set. This is because; most of classifiers are biased to the majority class, tend to ignore minority class and treat the mino...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd. Khairuddin, Ismail
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/id/eprint/32546/1/IsmailMohdKhairuddinMFKE2012.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.32546
record_format	uketd_dc
spelling	my-utm-ep.325462017-08-21T07:29:24Z Support vector machine for solving imbalanced dataset problem 2012 Mohd. Khairuddin, Ismail Q Science (General) Most of machine learning classifiers such as Neural Network (NN), Naïve Bayes, and Decision Tree Method C4.5 are failed to classify the data when it deals with imbalanced data set. This is because; most of classifiers are biased to the majority class, tend to ignore minority class and treat the minority class as a noise/disturbance/variance. Generally, to tackle the imbalanced data set problem it consists of two strategies which are data level and algorithm level. The data level method consist of features selection and re-sampling the data such as undersampling, oversampling and combination of both undersampling and oversampling, while for algorithm level it consist internal modification of learning program. In this project, the Support Vector Machine (SVM) classifier is proposed in order to investigate the imbalanced data set problem. The investigation is obtained by measured the performance based on SVM classifier. This investigation will cover and measure the performance SVM classifier by measuring the g-mean value. The performance of SVM classifier is measured by measuring the g-mean value .Therefore, in order to increase the performance of SVM classifier oversampling methods called SMOTE is introduced and combine with it and the g-mean value is calculated. Experimental validation on the proposed algorithm is performed and demonstrated on various set of imbalanced data sets. Some experiment have been design to validate the proposed algorithm and performed it with various set of imbalanced data sets. Finally, the result is for each proposed algorithm is being compared and analyze. 2012 Thesis http://eprints.utm.my/id/eprint/32546/ http://eprints.utm.my/id/eprint/32546/1/IsmailMohdKhairuddinMFKE2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69024?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Mohd. Khairuddin, Ismail Support vector machine for solving imbalanced dataset problem
description	Most of machine learning classifiers such as Neural Network (NN), Naïve Bayes, and Decision Tree Method C4.5 are failed to classify the data when it deals with imbalanced data set. This is because; most of classifiers are biased to the majority class, tend to ignore minority class and treat the minority class as a noise/disturbance/variance. Generally, to tackle the imbalanced data set problem it consists of two strategies which are data level and algorithm level. The data level method consist of features selection and re-sampling the data such as undersampling, oversampling and combination of both undersampling and oversampling, while for algorithm level it consist internal modification of learning program. In this project, the Support Vector Machine (SVM) classifier is proposed in order to investigate the imbalanced data set problem. The investigation is obtained by measured the performance based on SVM classifier. This investigation will cover and measure the performance SVM classifier by measuring the g-mean value. The performance of SVM classifier is measured by measuring the g-mean value .Therefore, in order to increase the performance of SVM classifier oversampling methods called SMOTE is introduced and combine with it and the g-mean value is calculated. Experimental validation on the proposed algorithm is performed and demonstrated on various set of imbalanced data sets. Some experiment have been design to validate the proposed algorithm and performed it with various set of imbalanced data sets. Finally, the result is for each proposed algorithm is being compared and analyze.
format	Thesis
qualification_level	Master's degree
author	Mohd. Khairuddin, Ismail
author_facet	Mohd. Khairuddin, Ismail
author_sort	Mohd. Khairuddin, Ismail
title	Support vector machine for solving imbalanced dataset problem
title_short	Support vector machine for solving imbalanced dataset problem
title_full	Support vector machine for solving imbalanced dataset problem
title_fullStr	Support vector machine for solving imbalanced dataset problem
title_full_unstemmed	Support vector machine for solving imbalanced dataset problem
title_sort	support vector machine for solving imbalanced dataset problem
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	http://eprints.utm.my/id/eprint/32546/1/IsmailMohdKhairuddinMFKE2012.pdf
_version_	1747816028420702208

Support vector machine for solving imbalanced dataset problem

Similar Items