IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v145y2020ics0167947320300025.html
   My bibliography  Save this article

Active learning in multiple-class classification problems via individualized binary models

Author

Listed:
  • Li, Jingjing
  • Chen, Zimu
  • Wang, Zhanfeng
  • Chang, Yuan-chin Ivan

Abstract

We propose a unified algorithm for both categorical and ordinal labeled data in multiclass classification problems, where each subject belongs to one class only. In training an effective classification rule, it is critical that one have and rely on a sufficient amount of reliably labeled data. As information on the training sample sizes needed to obtain satisfactory performance is lacking, we adopt an adaptive subject recruiting scheme with an experimental design criterion to shorten the training process. Because this kind of active learning method is naturally conducted in a sequential manner, we adopt sequential analysis to control the required sample size and ensure the performance of the final classifier. Additionally, we report its statistical properties and numerical results from using synthesized and real data.

Suggested Citation

  • Li, Jingjing & Chen, Zimu & Wang, Zhanfeng & Chang, Yuan-chin Ivan, 2020. "Active learning in multiple-class classification problems via individualized binary models," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:csdana:v:145:y:2020:i:c:s0167947320300025
    DOI: 10.1016/j.csda.2020.106911
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947320300025
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2020.106911?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. Deng, Xinwei & Joseph, V. Roshan & Sudjianto, Agus & Wu, C. F. Jeff, 2009. "Active Learning Through Sequential Design, With Applications to Detection of Money Laundering," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 969-981.
    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. Hsu, Hsiang-Ling & Chang, Yuan-chin Ivan & Chen, Ray-Bing, 2019. "Greedy active learning algorithm for logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 119-134.
    2. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    3. Bo-Shiang Ke & Yuan-chin Ivan Chang, 2021. "A Model-Free Subject Selection Method for Active Learning Classification Procedures," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 544-555, October.

    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:csdana:v:145:y:2020:i:c:s0167947320300025. 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/locate/csda .

    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.