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Model Similarity and Rank-Order Based Classification of Bayesian Networks

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  • Sung-Ho Kim
  • Geonyoup Noh

Abstract

Suppose that we rank-order the conditional probabilities for a group of subjects that are provided from a Bayesian network (BN) model of binary variables. The conditional probability is the probability that a subject has a certain attribute given an outcome of some other variables and the classification is based on the rank-order. Under the condition that the class sizes are equal across the class levels and that all the variables in the model are positively associated with each other, we compared the classification results between models of binary variables which share the same model structure. In the comparison, we used a BN model, called a similar BN model, which was constructed under some rule based on a set of BN models satisfying certain conditions. Simulation results indicate that the agreement level of the classification between a set of BN models and their corresponding similar BN model is considerably high with the exact agreement for about half of the subjects or more and the agreement up to one-class-level difference for about 90% or more. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jclass:v:30:y:2013:i:3:p:428-452
    DOI: 10.1007/s00357-013-9140-9
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    References listed on IDEAS

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    1. Kim, Sung-Ho, 2002. "Calibrated initials for an EM applied to recursive models of categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 97-110, July.
    2. H. Messatfa, 1992. "An algorithm to maximize the agreement between partitions," Journal of Classification, Springer;The Classification Society, vol. 9(1), pages 5-15, January.
    3. Robert Mislevy, 1994. "Evidence and inference in educational assessment," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 439-483, December.
    4. Ahmed N. Albatineh & Magdalena Niewiadomska-Bugaj & Daniel Mihalko, 2006. "On Similarity Indices and Correction for Chance Agreement," Journal of Classification, Springer;The Classification Society, vol. 23(2), pages 301-313, September.
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    Cited by:

    1. 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.

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