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Conference papers
Fast Bayesian Network Structure Learning using Quasi-Determinism Screening
Abstract : Learning the structure of Bayesian networks from data is a NP-Hard problem that
involves optimization over a super-exponential sized space. In this work, we show that in most
real life datasets, a number of the arcs contained in the final structure can be pre-screened
at low computational cost with a limited impact on the global graph score. We formalize the
identification of these arcs via the notion of quasi-determinism, and propose an associated
algorithm that narrows the structure learning task down to a subset of the original variables.
We show, on diverse benchmark datasets, that this algorithm exhibits a significant decrease in
computational time and complexity for only a little decrease in performance score.
Cited literature [17 references]
https://hal.archives-ouvertes.fr/hal-01691217
Contributor : Florence Forbes Connect in order to contact the contributor
Submitted on : Friday, January 4, 2019 - 6:52:30 PM
Last modification on : Wednesday, November 3, 2021 - 5:10:27 AM
Long-term archiving on: : Friday, April 5, 2019 - 12:25:47 PM
Contributor : Florence Forbes Connect in order to contact the contributor
Submitted on : Friday, January 4, 2019 - 6:52:30 PM
Last modification on : Wednesday, November 3, 2021 - 5:10:27 AM
Long-term archiving on: : Friday, April 5, 2019 - 12:25:47 PM
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