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Rough support vector regression
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DOI：10.1016/j.ejor.2009.10.023.png)
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Abstract
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This paper describes the relationship between support vector regression (SVR) and rough (or interval) patterns. SVR is the prediction component of the support vector techniques. Rough patterns are based on the notion of rough values, which consist of upper and lower bounds, and are used to effectively represent a range of variable values. Predictions of rough values in a variety of different forms within the context of interval algebra and fuzzy theory are attracting research interest. An extension of SVR, called rough support vector regression (RSVR), is proposed to improve the modeling of rough patterns. In particular, it is argued that the upper and lower bounds should be modeled separately. The proposal is shown to be a more flexible version of lower possibilistic regression model using epsilon-insensitivity. Experimental results on the Dow Jones Industrial Average demonstrate the suggested RSVR modeling technique. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Rough set
Rough value
Support vector machine
Prediction
Possiblistic regression
Support vector regression
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