Title :
Possibilistic regression analysis by support vector machine
Author_Institution :
Dept. of Inf. Manage., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan
Abstract :
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 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 SVM also uses quadratic programming approach whose advantage in interval regression analysis is to be able to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. The proposed algorithm is a 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.
Keywords :
data handling; linear programming; pattern recognition; quadratic programming; regression analysis; set theory; support vector machines; SVM; data set; high dimensional feature space; interval linear regression function estimation; interval nonlinear regression analysis; interval nonlinear regression model; linear programming; model-free method; nonlinear interval data modeling; pattern recognition; possibilistic regression analysis; quadratic programming; support vector machine; underlying model function; Data models; Estimation; Kernel; Linear regression; Support vector machines; Vectors; Interval regression analysis; Quadratic programming; Support vector machines (SVMs); Support vector regression machines; possibility and necessity models;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007433