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Title: Latent variable discovery in classification models
Authors: Zhang, Nevin Lianwen
Nielsen, Thomas D.
Jensen, Finn V.
Keywords: Naive Bayes model
Bayesian networks
Latent variables
Scientific discovery
Issue Date: 2003
Citation: To be publish in Artifical Intelligence in Medicine
Abstract: 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.
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