Latent variable discovery in classification models

  title={Latent variable discovery in classification models},
  author={Nevin Lianwen Zhang and Thomas D. Nielsen and Finn Verner Jensen},
  journal={Artificial intelligence in medicine},
  volume={30 3},
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… CONTINUE READING
Highly Cited
This paper has 35 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 22 extracted citations

Semi-hierarchical naïve Bayes classifier

2016 International Joint Conference on Neural Networks (IJCNN) • 2016
View 5 Excerpts
Highly Influenced

Semi-naive Bayesian Classification

View 6 Excerpts
Highly Influenced

Learning hidden variables in Bayesian Networks with Bayesian Entropy Criterion for supervised classification

2010 International Conference on Audio, Language and Image Processing • 2010
View 4 Excerpts
Highly Influenced

Transportation choice modeling on commuters in Jabodetabek using Bayesian network and polytomous logistic regression

2018 International Conference on Information and Communications Technology (ICOIACT) • 2018
View 1 Excerpt

Similar Papers

Loading similar papers…