Uncertainty in Artificial Intelligence
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Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification
Brian Ziebart, Anind Dey, J Bagnell
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 Na─▒ve 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.
Pages: 458-465
PS Link:
PDF Link: /papers/07/p458-ziebart.pdf
AUTHOR = "Brian Ziebart and Anind Dey and J Bagnell",
TITLE = "Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "458--465"

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