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Generalized Support Vector Quantile Regression

일반화 서포트벡터 분위수회귀에 대한 연구

  • Lee, Dongju (Industrial & Systems Engineering, Kongju National University) ;
  • Choi, Sujin (Department of Metal Mould Design, Korea Polytechnic)
  • 이동주 (공주대학교 산업시스템공학과) ;
  • 최수진 (한국폴리텍대학 스마트융합금형과)
  • Received : 2020.10.24
  • Accepted : 2020.12.16
  • Published : 2020.12.31

Abstract

Support vector regression (SVR) is devised to solve the regression problem by utilizing the excellent predictive power of Support Vector Machine. In particular, the ⲉ-insensitive loss function, which is a loss function often used in SVR, is a function thatdoes not generate penalties if the difference between the actual value and the estimated regression curve is within ⲉ. In most studies, the ⲉ-insensitive loss function is used symmetrically, and it is of interest to determine the value of ⲉ. In SVQR (Support Vector Quantile Regression), the asymmetry of the width of ⲉ and the slope of the penalty was controlled using the parameter p. However, the slope of the penalty is fixed according to the p value that determines the asymmetry of ⲉ. In this study, a new ε-insensitive loss function with p1 and p2 parameters was proposed. A new asymmetric SVR called GSVQR (Generalized Support Vector Quantile Regression) based on the new ε-insensitive loss function can control the asymmetry of the width of ⲉ and the slope of the penalty using the parameters p1 and p2, respectively. Moreover, the figures show that the asymmetry of the width of ⲉ and the slope of the penalty is controlled. Finally, through an experiment on a function, the accuracy of the existing symmetric Soft Margin, asymmetric SVQR, and asymmetric GSVQR was examined, and the characteristics of each were shown through figures.

Keywords

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