Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22152-1_35 | ECSQARU |
Keywords | Field | DocType |
complex relationship,uci data set,novel generative model,good classification accuracy,bayes rule,true distribution,latent tree classifier,class-conditional distribution,latent tree model,interesting subgroup,latent variable model | Data mining,Data set,Computer science,Bayesian network classifier,Latent variable model,Decision tree model,Latent class model,Artificial intelligence,Classifier (linguistics),Machine learning,Generative model,Bayes' theorem | Conference |
Citations | PageRank | References |
2 | 0.38 | 12 |
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 |