Journal of Automation and Information Sciences
Published 12 issues per year
ISSN Print: 1064-2315
ISSN Online: 2163-9337
SJR:
0.173
SNIP:
0.588
CiteScore™::
2
Indexed in
Fast Algorithm for Learning the Bayesian Networks From Data
Volume 43,
Issue 10, 2011,
pp. 1-9
DOI: 10.1615/JAutomatInfScien.v43.i10.10
ABSTRACT
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.
KEY WORDS: Bayesian networks structures, fast algorithm of searching from data, new constrained-based approach algorithm, rules of acceleration of inductive inference
CITED BY
-
Balabanov O.S., Knowledge discovery in data and causal models in analytical informatics, PROBLEMS IN PROGRAMMING, 3, 2017. Crossref
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