RT Journal Article
ID 2340ab6e7d7f56b4
A1 Balabanov, Alexander S.
A1 Gapyeyev , Alexander S.
A1 Gupal, Anatoliy M.
A1 Rzhepetskiy, Sergey S.
T1 Fast Algorithm for Learning the Bayesian Networks From Data
JF Journal of Automation and Information Sciences
JO JAI(S)
YR 2011
FD 2011-11-01
VO 43
IS 10
SP 1
OP 9
K1 Bayesian networks structures
K1 fast algorithm of searching from data
K1 new constrained-based approach algorithm
K1 rules of acceleration of inductive inference
AB The new constraint-based algorithm for learning dependency structures from data is developed. The novelty of the proposed algorithm is conditioned by the rules of acceleration of inductive inference, which drastically reduce the search area of separators while derivation of the model skeleton. On examples of the Bayesian networks of moderate saturation we have demonstrated that proposed algorithm learns Bayesian nets (of moderate density) multiple times faster than well-known PC algorithm.
PB Begell House
LK http://dl.begellhouse.com/journals/2b6239406278e43e,72b6dbf6512c0c65,2340ab6e7d7f56b4.html