title: Latent variable discovery in classification models.
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.
authors.affiliation: Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong, PR China. lzhang@cs.ust.hk
authors.forename: Nevin L
authors.initials: NL
authors.lastname: Zhang
authors.forename: Thomas D
authors.initials: TD
authors.lastname: Nielsen
authors.forename: Finn V
authors.initials: FV
authors.lastname: Jensen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002965:Classification; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D016008:Statistical Distributions