New computational methods for classification problems in the existence of outliers based on conic quadratic optimization
Yükleniyor...
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor and Francis Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Most 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.
Açıklama
Anahtar Kelimeler
Classification, Convex programming, Robust estimator, Tikhonov regularization, Mean -shift outlier model
Kaynak
Communications in Statistics: Simulation and Computation
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
49
Sayı
3
Künye
Özkurt, F. Y. ve Taylan, P. (2020). New computational methods for classification problems in the existence of outliers based on conic quadratic optimization. Communications in Statistics: Simulation and Computation, 49(3), 753-770.
