TY  - JOUR
T1  - Interval regression analysis using quadratic loss support vector machine.
JF  - IEEE Transactions on fuzzy systems
A1  - Dug Hun Hong
LA  - English
UL  - https://tuklas.up.edu.ph/Record/UP-99796217609517298
AB  - Support vector machines (SVMs) have been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of quadratic loss SVM. This version of SVM utilizes quadratic loss function, unlike the traditional SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. The quadratic loss SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. However, this is not a computationally expensive way. The quadratic loss SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. The proposed algorithm is a very attractive approach to modeling nonlinear interval data, and is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.
KW  - Fuzzy regression analysis.
KW  - High dimensional feature space.
KW  - Interval regression analysis.
KW  - Quadratic loss function.
KW  - Quadratic programming.
KW  - Support vector machine.
ER  -