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Fuzzy cost support vector regression on the fuzzy samples

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Abstract

This paper presents a new version of support vector regression (SVR) named Fuzzy Cost SVR (FCSVR) with a unique property of operating on fuzzy data where fuzzy cost (fuzzy margin and fuzzy penalty) are maximized. This idea admits to have uncertainty in the penalty and margin terms jointly. Robustness against noise is shown to be superior in the experimental results as a property compared with conventional SVR.

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Authors and Affiliations

  1. Computer Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Abedin Vahedian & Hadi Sadoghi Yazdi

  2. Electrical and Computer Engineering Department, Shahid Beheshti University of Tehran, Tehran, Iran

    Mehri Sadoghi Yazdi

  3. Mathematics Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Sohrab Effati

Authors
  1. Abedin Vahedian
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  2. Mehri Sadoghi Yazdi
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  3. Sohrab Effati
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  4. Hadi Sadoghi Yazdi
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Corresponding author

Correspondence to Hadi Sadoghi Yazdi.

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Vahedian, A., Sadoghi Yazdi, M., Effati, S. et al. Fuzzy cost support vector regression on the fuzzy samples. Appl Intell 35, 428–435 (2011). https://doi.org/10.1007/s10489-010-0232-5

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  • DOI: https://doi.org/10.1007/s10489-010-0232-5

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