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      • Theses - Department of Industrial Engineering
      • Dept. of Industrial Engineering - Master's degree
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      Risk-averse multi-class support vector machines

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      Author(s)
      Karagöz, Ayşenur
      Advisor
      İyigün, Özlem Çavuş
      Date
      2018-12
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      A 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.
      Keywords
      Support Vector Machines
      Multi-Class Classification Problem
      Riskaversion
      Conditional Value-at-Risk
      Value-at-Risk
      Permalink
      http://hdl.handle.net/11693/48221
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      • Dept. of Industrial Engineering - Master's degree 355
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