IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v36y2019i1d10.1007_s00357-018-9282-x.html
   My bibliography  Save this article

A New Method for Classifying Random Variables Based on Support Vector Machine

Author

Listed:
  • Maryam Abaszade

    (Ferdowsi University of Mashhad)

  • Sohrab Effati

    (Ferdowsi University of Mashhad)

Abstract

In this paper, a new version of Support Vector Machine (SVM) is proposed which any of training samples are considered the random variables. Hence, in order to achieve robustness, the constraint in SVM must be replaced with probability of constraint. In this new model, by applying the nonparametric statistical methods, we obtain the optimal separating hyperplane by solving a quadratic optimization problem. Afterwards, we present the least squares model of our proposed method. The efficiency of our proposed method is shown by several examples for both cases (linear and nonlinear) with probabilistic constraints.

Suggested Citation

  • Maryam Abaszade & Sohrab Effati, 2019. "A New Method for Classifying Random Variables Based on Support Vector Machine," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 152-174, April.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:1:d:10.1007_s00357-018-9282-x
    DOI: 10.1007/s00357-018-9282-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-018-9282-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-018-9282-x?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. Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Douglas L. Steinley, 2019. "Editorial: Journal of Classification Vol. 36-3," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 393-396, October.

    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. Ximing Wang & Neng Fan & Panos M. Pardalos, 2018. "Robust chance-constrained support vector machines with second-order moment information," Annals of Operations Research, Springer, vol. 263(1), pages 45-68, April.
    2. Petros Xanthopoulos & Mario Guarracino & Panos Pardalos, 2014. "Robust generalized eigenvalue classifier with ellipsoidal uncertainty," Annals of Operations Research, Springer, vol. 216(1), pages 327-342, May.
    3. Miyashiro, Ryuhei & Takano, Yuichi, 2015. "Mixed integer second-order cone programming formulations for variable selection in linear regression," European Journal of Operational Research, Elsevier, vol. 247(3), pages 721-731.
    4. Gianluca Gazzola & Myong K. Jeong, 2021. "Support vector regression for polyhedral and missing data," Annals of Operations Research, Springer, vol. 303(1), pages 483-506, August.
    5. Lin, Fengming & Fang, Shu-Cherng & Fang, Xiaolei & Gao, Zheming & Luo, Jian, 2024. "A distributionally robust chance-constrained kernel-free quadratic surface support vector machine," European Journal of Operational Research, Elsevier, vol. 316(1), pages 46-60.
    6. Couellan, Nicolas & Wang, Wenjuan, 2017. "Uncertainty-safe large scale support vector machines," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 215-230.
    7. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    8. Takeda, Akiko & Kanamori, Takafumi, 2009. "A robust approach based on conditional value-at-risk measure to statistical learning problems," European Journal of Operational Research, Elsevier, vol. 198(1), pages 287-296, October.
    9. Peter Tsyurmasto & Michael Zabarankin & Stan Uryasev, 2014. "Value-at-risk support vector machine: stability to outliers," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 218-232, July.
    10. Wenxin Zhu & Yunyan Song & Yingyuan Xiao, 2018. "A New Support Vector Machine Plus with Pinball Loss," Journal of Classification, Springer;The Classification Society, vol. 35(1), pages 52-70, April.
    11. Mohammad Poursaeidi & O. Kundakcioglu, 2014. "Robust support vector machines for multiple instance learning," Annals of Operations Research, Springer, vol. 216(1), pages 205-227, May.
    12. 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.
    13. Chen, Yan-Cheng & Su, Chao-Ton, 2016. "Distance-based margin support vector machine for classification," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 141-152.
    14. Cassioli, A. & Chiavaioli, A. & Manes, C. & Sciandrone, M., 2013. "An incremental least squares algorithm for large scale linear classification," European Journal of Operational Research, Elsevier, vol. 224(3), pages 560-565.
    15. Hossein Kamalzadeh & Saeid Nassim Sobhan & Azam Boskabadi & Mohsen Hatami & Amin Gharehyakheh, 2019. "Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines," Papers 1912.02373, arXiv.org.

    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:spr:jclass:v:36:y:2019:i:1:d:10.1007_s00357-018-9282-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.