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Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points

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Listed:
  • Waleed Dhhan
  • Habshah Midi
  • Thaera Alameer

Abstract

Support vector regression is used to evaluate the linear and non-linear relationships among variables. Although it is non-parametric technique, it is still affected by outliers, because the possibility to select them as support vectors. In this article, we proposed a robust support vector regression for linear and nonlinear target functions. In order to carry out this goal, the support vector regression model with fixed parameters is used to detect and minimize the effects of abnormal points in the data set. The efficiency of the proposed method is investigated by using real and simulation examples.

Suggested Citation

  • Waleed Dhhan & Habshah Midi & Thaera Alameer, 2017. "Robust Support Vector Regression Model in the Presence of Outliers and Leverage Points," Modern Applied Science, Canadian Center of Science and Education, vol. 11(8), pages 1-92, August.
    • Handle: RePEc:ibn:masjnl:v:11:y:2017:i:8:p:92
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    References listed on IDEAS

    as
    1. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    2. Waleed Dhhan & Sohel Rana & Habshah Midi, 2015. "Non-sparse ϵ -insensitive support vector regression for outlier detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1723-1739, August.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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