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A latent class method for the selection of prototypes using expert ratings
Author(s) -
Miller William E.
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4399
Subject(s) - latent class model , class (philosophy) , computer science , selection (genetic algorithm) , artificial intelligence , probabilistic latent semantic analysis , machine learning , latent variable , affine transformation , statistics , econometrics , data mining , mathematics , pure mathematics
Latent class analysis can be applied to the outcomes of expert ratings to select objects or subjects that are regarded as prototypical of a category in an ordinal classification system. During a pilot study, Monte Carlo simulations demonstrated that the probability of correct selection is larger when using latent class analysis than when using methods that rely on agreement statistics. Further improvements in the latent class results can also be achieved by applying affine transformations to latent class estimates of sensitivity and specificity. An application is presented that involves the selection of prototypical radiographs. Copyright © 2011 John Wiley & Sons, Ltd.