Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ijar.2012.06.024 | Int. J. Approx. Reasoning |
Keywords | Field | DocType |
latent tree approach,good classification accuracy,classification accuracy,true distribution,uci data,unsupervised learning,latent tree classifier,supervised learning,class-conditional distribution,latent tree model,novel generative classifier | Data mining,Decision tree model,Latent class model,Supervised learning,Unsupervised learning,Artificial intelligence,Probabilistic latent semantic analysis,Cluster analysis,Classifier (linguistics),Machine learning,Mathematics,Bayes' theorem | Journal |
Volume | Issue | ISSN |
54 | 4 | 0888-613X |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Wang | 1 | 64 | 5.86 |
| Nevin .L Zhang | 2 | 895 | 97.21 |
| Tao Chen | 3 | 76 | 7.04 |
| Leonard K. M. Poon | 4 | 94 | 10.96 |