Support Vector Regression for imprecise data

Emilio Carrizosa (ecarrizosaus.es)
José Gordillo (jgordillous.es)
Frank Plastria (Frank.Plastriavub.ac.be)

Abstract: In this work, a regression problem is studied where the elements of the database are sets with certain geometrical properties. In particular, our model can be applied to handle data affected by some kind of noise or uncertainty and interval-valued data, and databases with missing values as well. The proposed formulation is based on the standard epsilon-Support Vector Regression approach. In the interval-data case, two different formulations will be obtained, according to the way of measuring the distance between the prediction and the actual intervals. Computational experiments with real databases are performed.

Keywords: Support Vector Regression, Interval Data, Missing Values, Quadratic Programming, Robustness, Gauges.

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Nonlinear Optimization (Quadratic Programming )

Citation: Technical Report MOSI/35, MOSI Department, Vrije Universiteit Brussel, October 2007

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Entry Submitted: 11/05/2007
Entry Accepted: 11/05/2007
Entry Last Modified: 01/31/2008

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