Yerlikaya Özkurt, FatmaYerlikaya-Ozkurt, FatmaTaylan, PakizeIndustrial Engineering2024-07-052024-07-05202080361-09181532-414110.1080/03610918.2019.16614772-s2.0-85073816123https://doi.org/10.1080/03610918.2019.1661477https://hdl.handle.net/20.500.14411/3318Most of the statistical research involves classification which is a procedure utilized to establish prediction models to set apart and classify new observations in the dataset from every fields of science, technology, and economics. However, these models may give misclassification results when dataset contains outliers (extreme data points). Therefore, we dealt with outliers in classification problem: firstly, by combining robustness of mean-shift outlier model and then stability of Tikhonov regularization based on continuous optimization method called Conic Quadratic Programming. These new methodologies are performed on classification dataset within the existence of outliers, and the results are compared with parametric model by using well-known performance measures.eninfo:eu-repo/semantics/closedAccessMean -shift Outlier ModelClassificationConvex programmingTikhonov regularizationRobust estimatorArticleQ4493753770WOS:000486705000001