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Non-sparse ϵ -insensitive support vector regression for outlier detection

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  • Waleed Dhhan
  • Sohel Rana
  • Habshah Midi

Abstract

To estimate the approximate relationship between the dependent variable and its independent variables, it is necessary to diagnose outliers commonly present in numerous real applications before constructing the model. Nevertheless, the techniques of the standard support vector regression ( -SVR) and modified support vector regression ( ) achieved good performance for outliers' detection for nonlinear functions with high-dimensional inputs. However, they still suffer from the costs of time and the setting of parameters. In this study, we propose a practical method for detecting outliers, using non-sparse -SVR, which minimizes time cost and introduces fixed parameters. We apply this approach for real and simulation data sets to test its effectiveness.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1723-1739
    DOI: 10.1080/02664763.2015.1005064
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    References listed on IDEAS

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    1. Walid Gani & Hassen Taleb & Mohamed Limam, 2010. "Support vector regression based residual control charts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 309-324.
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    Cited by:

    1. Sohel RANA, & Waleed DHHAN & Habshah MIDI, 2016. "A Hybrid Methodology Based on Dynamic Programming and Simulated Annealing for Solving an Integrated Cell Formation and Layout Problem," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 231-246.
    2. 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.

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