Latent variable discovery in classification models

Authors:
Nevin L Zhang;Thomas D Nielsen;Finn V Jensen
Affiliations:
Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong, PR China;Department of Computer Science, Aalborg University, Aalborg, Denmark;Department of Computer Science, Aalborg University, Aalborg, Denmark
Venue:
Artificial Intelligence in Medicine
Year:
2004

Citing 6
Cited 10

Using hidden nodes in Bayesian networks

Artificial Intelligence
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Semi-Naive Bayesian Classifier

EWSL '91 Proceedings of the European Working Session on Machine Learning
Learning Belief Networks in the Presence of Missing Values and Hidden Variables

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning the dimensionality of hidden variables

UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence

Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs

Machine Learning
Classification using Hierarchical Naïve Bayes models

Machine Learning
Methodological review: Knowledge discovery in traditional Chinese medicine: State of the art and perspectives

Artificial Intelligence in Medicine
Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility

Applied Soft Computing
Latent Variable Models for Causal Knowledge Acquisition

CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
A hierarchical Naïve Bayes model for approximate identity matching

Decision Support Systems
Latent tree classifier

ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Incorporating expert knowledge when learning Bayesian network structure: A medical case study

Artificial Intelligence in Medicine
LTC: A latent tree approach to classification

International Journal of Approximate Reasoning
A survey on latent tree models and applications

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

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.