Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/778

Latent variable discovery in classification models

Authors Zhang, NL
Nielsen, TD
Jensen, FV
Issue Date 2004
Source Artificial intelligence in medicine , v. 30, (3), 2004, MAR, p. 283-299
Summary The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical applications, because it can lead to a better understanding of application domains. It can also improve classification accuracy and boost user confidence in classification models. (C) 2004 Elsevier B.V. All rights reserved.
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ISSN 0933-3657
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Language English
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