Support vector interval regression machine for crisp input and output data

Authors:
Changha Hwang;Dug Hun Hong;Kyung Ha Seok
Affiliations:
Division of Information and Computer Science, Dankook University, Seoul 140 - 714, South Korea;Department of Mathematics, Myongji University, Kyunggido 449-728, South Korea;Department of Data Science, Inje University, Kyungnam 621-749, South Korea
Venue:
Fuzzy Sets and Systems
Year:
2006

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Abstract

Support vector regression (SVR) has been very successful in function estimation problems for crisp data. In this paper, we propose a robust method to evaluate interval regression models for crisp input and output data combining the possibility estimation formulation integrating the property of central tendency with the principle of standard SVR. The proposed method is robust in the sense that outliers do not affect the resulting interval regression. Furthermore, the proposed method is model-free method, since we do not have to assume the underlying model function for interval nonlinear regression model with crisp input and output. In particular, this method performs better and is conceptually simpler than support vector interval regression networks (SVIRNs) which utilize two radial basis function networks to identify the upper and lower sides of data interval. Five examples are provided to show the validity and applicability of the proposed method.