Support vector machine for solving small dataset problem

Data quantity is the main concern in the small data set problem, because usually insufficient data information will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. A computational technique called Supp...

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Main Author:	Abdul Rahman, Ahmad Rijal
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/id/eprint/32547/1/AhmadRijalAbdulRahmanMFKE2012.pdf
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id	my-utm-ep.32547
record_format	uketd_dc
spelling	my-utm-ep.325472017-08-21T07:35:19Z Support vector machine for solving small dataset problem 2012 Abdul Rahman, Ahmad Rijal Q Science (General) Data quantity is the main concern in the small data set problem, because usually insufficient data information will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. A computational technique called Support Vector Machine (SVM) constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks, is proposed for this project. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin). In general, the larger the margin the lower the generalization error of the classifier is achieved. In this research, Support Vector Machine (SVM) is employed for solving small dataset problems in binary classification. A lot of performance measure can be used to measure the performance of data. This research used accuracy as a performance measure. In order to improve the performance of accuracy, SMOTE (Synthetic Minority Oversampling Technique) algorithm has been used to balance the data with creates a synthetic data in the minority class for imbalanced dataset or both of negative and positive class for balanced dataset problem. An algorithm of SVM and SMOTE has been developed using Matlab. 2012 Thesis http://eprints.utm.my/id/eprint/32547/ http://eprints.utm.my/id/eprint/32547/1/AhmadRijalAbdulRahmanMFKE2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:72745?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) Abdul Rahman, Ahmad Rijal Support vector machine for solving small dataset problem
description	Data quantity is the main concern in the small data set problem, because usually insufficient data information will not lead to a robust classification performance. How to extract more effective information from a small data set is thus of considerable interest. A computational technique called Support Vector Machine (SVM) constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks, is proposed for this project. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin). In general, the larger the margin the lower the generalization error of the classifier is achieved. In this research, Support Vector Machine (SVM) is employed for solving small dataset problems in binary classification. A lot of performance measure can be used to measure the performance of data. This research used accuracy as a performance measure. In order to improve the performance of accuracy, SMOTE (Synthetic Minority Oversampling Technique) algorithm has been used to balance the data with creates a synthetic data in the minority class for imbalanced dataset or both of negative and positive class for balanced dataset problem. An algorithm of SVM and SMOTE has been developed using Matlab.
format	Thesis
qualification_level	Master's degree
author	Abdul Rahman, Ahmad Rijal
author_facet	Abdul Rahman, Ahmad Rijal
author_sort	Abdul Rahman, Ahmad Rijal
title	Support vector machine for solving small dataset problem
title_short	Support vector machine for solving small dataset problem
title_full	Support vector machine for solving small dataset problem
title_fullStr	Support vector machine for solving small dataset problem
title_full_unstemmed	Support vector machine for solving small dataset problem
title_sort	support vector machine for solving small 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/32547/1/AhmadRijalAbdulRahmanMFKE2012.pdf
_version_	1747816028663971840

Support vector machine for solving small dataset problem

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