IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v37y2020i3d10.1007_s00357-019-09340-6.html
   My bibliography  Save this article

A Short Note on Improvement of Agreement Rate

Author

Listed:
  • Doyeob Kim

    (Korea Advanced Institute of Science and Technology)

  • Sung-Ho Kim

    (Korea Advanced Institute of Science and Technology)

Abstract

Consider a rank-ordering problem, ranking a group of subjects by the conditional probability from a Bayesian network (BN) model of binary variables. The conditional probability is the probability that a subject is in a certain state given an outcome of some other variables. The classification is based on the rank order and the class levels are assigned with equal proportions. Two BN models are said to be similar to each other if they are of the same model structure but with different probability distributions each of which satisfies the positive association condition. Let ℳ ${\mathcal M}$ be a set of BN models which are similar to each other. We constructed a BN model M∗, which is similar to all the models in ℳ ${\mathcal M}$ and the best with regard to ℳ ${\mathcal M}$ in the sense of the Kullback-Leibler divergence measure. It is found by numerical experiments that, on average, the agreement rate of classifications between a model in ℳ ${\mathcal M}$ and the similar model M∗ is far larger than that by a random classification and the difference in agreement rate becomes more apparent as the class number increases.

Suggested Citation

  • Doyeob Kim & Sung-Ho Kim, 2020. "A Short Note on Improvement of Agreement Rate," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 550-557, October.
  • Handle: RePEc:spr:jclass:v:37:y:2020:i:3:d:10.1007_s00357-019-09340-6
    DOI: 10.1007/s00357-019-09340-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-019-09340-6
    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/s00357-019-09340-6?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. Sung-Ho Kim & Geonyoup Noh, 2013. "Model Similarity and Rank-Order Based Classification of Bayesian Networks," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 428-452, October.
    2. Robert Mislevy, 1994. "Evidence and inference in educational assessment," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 439-483, December.
    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. Daniel Segall, 2001. "General ability measurement: An application of multidimensional item response theory," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 79-97, March.
    2. Kim, Seong-Ho & Kim, Sung-Ho, 2006. "A divide-and-conquer approach in applying EM for large recursive models with incomplete categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 611-641, February.
    3. Sung-Ho Kim & Geonyoup Noh, 2013. "Model Similarity and Rank-Order Based Classification of Bayesian Networks," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 428-452, October.
    4. Younyoung Choi & Young Il Cho, 2020. "Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System," Sustainability, MDPI, vol. 12(19), pages 1-13, September.
    5. Russell G. Almond & Joris Mulder & Lisa A. Hemat & Duanli Yan, 2009. "Bayesian Network Models for Local Dependence Among Observable Outcome Variables," Journal of Educational and Behavioral Statistics, , vol. 34(4), pages 491-521, December.

    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:37:y:2020:i:3:d:10.1007_s00357-019-09340-6. 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.