IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v82y2012i3p528-534.html
   My bibliography  Save this article

A rough margin-based linear ν support vector regression

Author

Listed:
  • Xu, Yitian

Abstract

We propose a rough margin-based linear ν-SVR (rough linear ν-SVR) by introducing the rough set theory into the linear programming-based ν-support vector regression (linear ν-SVR), to deal with the problem of over-fitting. Double ϵs are utilized to construct the rough margin for the rough linear ν-SVR instead of the single ϵ used in the classical linear ν-SVR, and this rough margin is composed of a lower margin and upper margin. Therefore, more data points are adaptively considered in constructing the regressor than in the linear ν-SVR. Moreover, points lying in different positions are given different penalties. Specifically, points within the lower margin are given no penalty, and points in the rough boundary are given small penalties, while the points lying outside the upper margin are given larger penalties. Our proposed algorithm avoids the over-fitting problem to a certain extent. The experimental results on seven datasets demonstrate the feasibility and validity of our proposed algorithm.

Suggested Citation

  • Xu, Yitian, 2012. "A rough margin-based linear ν support vector regression," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 528-534.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:3:p:528-534
    DOI: 10.1016/j.spl.2011.11.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715211003609
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2011.11.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shang, Gang & Xu, Liyun & Tian, Jinzhu & Cai, Dongwei & Xu, Zhun & Zhou, Zhuo, 2023. "A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger," Energy, Elsevier, vol. 274(C).
    2. 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.
    3. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:82:y:2012:i:3:p:528-534. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.