Latent tree classifier

Authors:
Yi Wang;Nevin .L Zhang;Tao Chen;Leonard K. M. Poon
Affiliations:
Department of Computer Science, National University of Singapore, Singapore, Singapore;Department of Computer Science & Engineering, The Hong Kong University of Science & Technology, Kowloon, Hong Kong;Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Department of Computer Science & Engineering, The Hong Kong University of Science & Technology, Kowloon, Hong Kong
Venue:
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Year:
2011

Citing 15
Cited 2

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning

C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
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
Hierarchical Latent Class Models for Cluster Analysis

The Journal of Machine Learning Research
Not So Naive Bayes: Aggregating One-Dependence Estimators

Machine Learning
Latent Classification Models

Machine Learning
Classification using Hierarchical Naïve Bayes models

Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Latent classification models for binary data

Pattern Recognition
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Latent tree models for multivariate density estimation: algorithms and applications

Latent tree models for multivariate density estimation: algorithms and applications
A Bayesian network classifier that combines a finite mixture model and a naïve bayes model

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Latent variable discovery in classification models

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

We propose a novel generative model for classification called latent tree classifier (LTC). An LTC represents each class-conditional distribution of attributes using a latent tree model, and uses Bayes rule to make prediction. Latent tree models can capture complex relationship among attributes. Therefore, LTC can approximate the true distribution behind data well and thus achieve good classification accuracy. We present an algorithm for learning LTC and empirically evaluate it on 37 UCI data sets. The results show that LTC compares favorably to the state-of-the-art. We also demonstrate that LTC can reveal underlying concepts and discover interesting subgroups within each class.