Wu, JingjingZhang, QingrunHuang, Qian2024-09-102024-09-102024-09-08Huang, Q. (2024). Distributionally robust binary classifier under Wasserstein distance (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/119671The robustification of statistical models has been a popular topic for decades. Statistical robustification and robust optimization are the two main approaches in the literature, where the former stabilizes the model output by removing the outlier points while the latter concerns more the outlier points in making the conservative decisions. This thesis develops a novel robust optimization perspective to robustify a class of binary classifiers. Our model considers the worst-case distribution within a pre-determined uncertainty ball that centers at the given benchmark distribution with the radius calculated as per the Wasserstein distance. We derive the tractable formulation for the general problem. When focusing on the support vector machine (SVM), the general problem boils down to an easy-to-solve second- order cone programming problem. The robustified SVM is then applied to synthetic data with and without contamination, and our simulation studies show that our robustified SVM model can outperform the classical SVM and the extreme empirical loss SVM models under many circumstances.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Distributional robustnessBinary classifierSupport vector machineWasserstein distanceEducation--Mathematicsmaster thesis