Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume 30, Issue 3, Pages 283-299Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.artmed.2003.11.004
Keywords
naive Bayes model; Bayesian networks; latent variables; learning; scientific discovery
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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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