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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
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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.

CITED BY
  1. Balabanov O.S., Knowledge discovery in data and causal models in analytical informatics, PROBLEMS IN PROGRAMMING, 3, 2017. Crossref

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