A Short Note on Improvement of Agreement Rate

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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. LetMbe a set of BN models which are similar to each other. We constructed a BN model M*, which is similar to all the models inMand the best with regard toMin 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 inMand 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.
Publisher
SPRINGER
Issue Date
2020-10
Language
English
Article Type
Article
Citation

JOURNAL OF CLASSIFICATION, v.37, no.3, pp.550 - 557

ISSN
0176-4268
DOI
10.1007/s00357-019-09340-6
URI
http://hdl.handle.net/10203/279305
Appears in Collection
MA-Journal Papers(저널논문)
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