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Rough support vector regression

Author

Listed:
  • Lingras, P.
  • Butz, C.J.

Abstract

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.

Suggested Citation

  • Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, October.
  • Handle: RePEc:eee:ejores:v:206:y:2010:i:2:p:445-455
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    References listed on IDEAS

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    Cited by:

    1. Pedrycz, Witold, 2014. "Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing," European Journal of Operational Research, Elsevier, vol. 232(1), pages 137-145.
    2. Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
    3. Xu, Yitian, 2012. "A rough margin-based linear ν support vector regression," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 528-534.

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