Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification

Brian D. Ziebart, Anind K. Dey, and J. Andrew Bagnell
Conference on Uncertainty in Artificial Intelligence (UAI 2007).
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Abstract: Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are tree-structures, and fixed-orderings with limited in-degree. We show how MAP estimates and BMA for selectively conditioned forests (SCF), a combination of these two classes, can be computed efficiently for ordered sets of variables. We apply SCFs to temporal data to learn Dynamic Bayesian Networks having an intra-timestep forest and inter-timestep limited in-degree structure, improving model accuracy over DBNs without the combination of structures. We also apply SCFs to Bayes Net classification to learn selective forest-augmented Naive Bayes classifiers. We argue that the built-in feature selection of selective augmented Bayes classifiers makes them preferable to similar non-selective classifiers based on empirical evidence.

Related Research Areas: Dependence learning, Tree structure learning, Dynamic Bayesian Network structure learning, time series structure learning, tree-augmented Naive Bayes, forest-augmented Naive Bayes

Bibtex:

@inproceedings{bziebart-scfs,
   author = {Brian D. Ziebart and Anind K. Dey and J. Andrew Bagnell},
   title = {Learning Selectively Conditioned Forest Structures with 
   Applications to DBNs and Classification},
   year = {2007},
   booktitle = {Proc. UAI},
   pages = {458--465}
}

Additional related work:
M. Siracusa and J. Fisher III. Tractable Bayesian Inference of Time-Series Dependence Structure. AISTATS 2009.