A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data
Denver Dash, Marek Druzdzel
We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) using a heuristic based on conventional constraint-based techniques. Each essential graph is then converted into a directed acyclic graph and scored using a Bayesian scoring metric. Two variants of the algorithm are developed and tested using data from randomly generated networks of sizes from 15 to 45 nodes with data sizes ranging from 250 to 2000 records. Both variations are compared to, and found to consistently outperform two variations of greedy search with restarts.
Keywords: Constraint-based learning, Bayesian learning
PS Link: http://www.sis.pitt.edu/~ddash/uai99.ps.zip
PDF Link: /papers/99/p142-dash.pdf
AUTHOR = "Denver Dash
and Marek Druzdzel",
TITLE = "A Hybrid Anytime Algorithm for the Constructiion of Causal Models From Sparse Data",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "142--149"