LTC: A latent tree approach to classification

Yi WANG, Nevin L. ZHANG, Tao CHEN, Kin Man POON

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Abstract

Latent tree models were proposed as a class of models for unsupervised learning, and have been applied to various problems such as clustering and density estimation. In this paper, we study the usefulness of latent tree models in another paradigm, namely supervised learning. We propose a novel generative classifier 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 is able to approximate the true distribution behind data well and thus achieves good classification accuracy. We present an algorithm for learning LTC and empirically evaluate it on an extensive collection of UCI data. The results show that LTC compares favorably to the state-of-the-art in terms of classification accuracy. We also demonstrate that LTC can reveal underlying concepts and discover interesting subgroups within each class. Copyright © 2012 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)560-572
JournalInternational Journal of Approximate Reasoning
Volume54
Issue number4
Early online dateJul 2012
DOIs
Publication statusPublished - Jun 2013

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Classifiers
Unsupervised learning
Supervised learning

Citation

Wang, Y., Zhang, N. L., Chen, T. and Poon, L. K. M. (2013). LTC: A latent tree approach to classification. International Journal of Approximate Reasoning, 54(4), 560-572. doi: 10.1016/j.ijar.2012.06.024.

Keywords

  • Bayesian network classifier
  • Latent tree model