Risk-averse multi-class support vector machines

Simple item page
buir.advisorİyigün, Özlem Çavuş
dc.contributor.authorKaragöz, Ayşenur
dc.date.accessioned2018-12-27T13:07:35Z
dc.date.available2018-12-27T13:07:35Z
dc.date.copyright2018-12
dc.date.issued2018-12
dc.date.submitted2018-12
dc.departmentDepartment of Industrial Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 97-101).en_US
dc.description.abstractA classification problem aims to identify the class of new observations based on the previous observations whose classes are known. It has many applications in a variety of disciplines such as medicine, finance and artificial intelligence. However, presence of outliers and noise in previous observations may have significant impact on the classification performance. Support vector machine (SVM) is a classifier introduced to solve binary classification problems under the presence of noise and outliers. In the literature, risk-averse SVM is shown to be more stable to noise and outliers compared to the original SVM formulations. However, we often observe more than two classes in real-life datasets. In this study, we aim to develop riskaverse multi-class SVMs following the idea of risk-averse binary SVM. We use risk measures, VaR and CVaR, to implement risk-aversion to multi-class SVMs. Since VaR constraints are nonconvex in general, SVMs with VaR constraints are more complex than SVMs with CVaR. Therefore, we propose a strong big-M formulation to solve multi-class SVM problems with VaR constraints efficiently. We also provide a computational study on the classification performance of the original multi-class SVM formulations and the proposed risk-averse formulations using artificial and real-life datasets. The results show that multi-class SVMs with VaR are more stable to outliers and noise compared to multi-class SVMs with CVaR, and both of them are more stable than the original formulations.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Ayşenur Karagöz.en_US
dc.format.extentxiv, 113 leaves : illustrations, charts (some color) ; 30 cm.en_US
dc.identifier.itemidB156799
dc.identifier.urihttp://hdl.handle.net/11693/48221
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectMulti-Class Classification Problemen_US
dc.subjectRiskaversionen_US
dc.subjectConditional Value-at-Risken_US
dc.subjectValue-at-Risken_US
dc.titleRisk-averse multi-class support vector machinesen_US
dc.title.alternativeRisk'ten kaçınan çok sınıflı destek vektör makinelerien_US
dc.typeThesisen_US
Files
Collections
Dept. of Industrial Engineering - Master's degree