Learning with Mixtures of Trees
Marina Meila, Michael I. Jordan;
This paper describes the mixtures-of-trees model, a probabilistic
model for discrete multidimensional domains. Mixtures-of-trees
generalize the probabilistic trees of Chow and Liu (1968) in a
different and complementary direction to that of Bayesian networks.
We present efficient algorithms for learning mixtures-of-trees
models in maximum likelihood and Bayesian frameworks.
We also discuss additional efficiencies that can be obtained
when data are "sparse," and we present data structures and
algorithms that exploit such sparseness. Experimental results
demonstrate the performance of the model for both density
estimation and classification. We also discuss the sense in
which tree-based classifiers perform an implicit form of feature
selection, and demonstrate a resulting insensitivity to irrelevant