IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v11y2017i3d10.1007_s11634-016-0253-y.html
   My bibliography  Save this article

A new approach for determining the prior probabilities in the classification problem by Bayesian method

Author

Listed:
  • Thao Nguyen-Trang

    (Ton Duc Thang University
    Ton Duc Thang University)

  • Tai Vo-Van

    (Can Tho University)

Abstract

In this article, we suggest a new algorithm to identify the prior probabilities for classification problem by Bayesian method. The prior probabilities are determined by combining the information of populations in training set and the new observations through fuzzy clustering method (FCM) instead of using uniform distribution or the ratio of sample or Laplace method as the existing ones. We next combine the determined prior probabilities and the estimated likelihood functions to classify the new object. In practice, calculations are performed by Matlab procedures. The proposed algorithm is tested by the three numerical examples including bench mark and real data sets. The results show that the new approach is reasonable and gives more efficient than existing ones.

Suggested Citation

  • Thao Nguyen-Trang & Tai Vo-Van, 2017. "A new approach for determining the prior probabilities in the classification problem by Bayesian method," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 629-643, September.
    • Handle: RePEc:spr:advdac:v:11:y:2017:i:3:d:10.1007_s11634-016-0253-y
    DOI: 10.1007/s11634-016-0253-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-016-0253-y
    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/s11634-016-0253-y?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. Tai Vo Van & T. Pham-Gia, 2010. "Clustering probability distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1891-1910.
    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. Pierpaolo D’Urso & María Ángeles Gil, 2017. "Fuzzy data analysis and classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 645-657, December.
    2. Tai Vo-Van & Ha Che-Ngoc & Nghiep Le-Dai & Thao Nguyen-Trang, 2022. "A New Strategy for Short-Term Stock Investment Using Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 887-911, February.

    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. Thao Nguyentrang & Tai Vovan, 2017. "Fuzzy clustering of probability density functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 583-601, March.
    2. Hieu Huynh-Van & Tuan Le-Hoang & Tai Vo-Van, 2024. "Classifying for images based on the extracted probability density function and the quasi Bayesian method," Computational Statistics, Springer, vol. 39(5), pages 2677-2701, July.
    3. Ha Che-Ngoc & Thao Nguyen-Trang & Tran Nguyen-Bao & Trung Nguyen-Thoi & Tai Vo-Van, 2022. "A new approach for face detection using the maximum function of probability density functions," Annals of Operations Research, Springer, vol. 312(1), pages 99-119, May.
    4. Tai Vovan & Dinh Phamtoan & Le Hoang Tuan & Thao Nguyentrang, 2021. "An automatic clustering for interval data using the genetic algorithm," Annals of Operations Research, Springer, vol. 303(1), pages 359-380, August.
    5. Tai VoVan & Thao Nguyen Trang, 2018. "Similar Coefficient of Cluster for Discrete Elements," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 19-36, May.

    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:advdac:v:11:y:2017:i:3:d:10.1007_s11634-016-0253-y. 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.