Interval regression analysis using support vector machine and quantile regression

Authors:
Changha Hwang;Dug Hun Hong;Eunyoung Na;Hyejung Park;Jooyong Shim
Affiliations:
Division of Information and Computer Sciences, Dankook University, Yongsan Seoul, South Korea;Department of Mathematics, Myongji University, Yongin Kyunggido, South Korea;Department of Statistical Information, Catholic University of Daegu, Kyungbuk, South Korea;Department of Statistical Information, Catholic University of Daegu, Kyungbuk, South Korea;Corresponding Author, Department of Statistics, Catholic University of Daegu, Kyungbuk, South Korea
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

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Abstract

This paper deals with interval regression analysis using support vector machine and quantile regression method. The algorithm consists of two phases – the identification of the main trend of the data and the interval regression based on acquired main trend. Using the principle of support vector machine the linear interval regression can be extended to the nonlinear interval regression. Numerical studies are then presented which indicate the performance of this algorithm.