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An analytical threshold for combining Bayesian Networks
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DOI：10.1016/j.knosys.2019.03.014.png)
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摘要
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Structural learning of Bayesian Networks (BNs) is an NP-hard problem, and the use of sub-optimal strategies is essential in domains involving many variables. One approach is the generation of multiple approximate structures and then reduce the ensemble to a representative structure. This can be performed by using the occurrence frequency (on the structures ensemble) as the criteria for accepting a dominant directed edge between two nodes and thus obtaining the single structure. In this paper, an analogy with an adapted one-dimensional random-walk was used for analytically deducing an appropriate decision threshold to such occurrence frequency. The obtained closed-form expression has been validated across the synthetic datasets applying the Matthews Correlation Coefficient (MCC) as the performance metric. In the experiments using a recent medical dataset, the resulting BN from the analytical cutoff-frequency captured the expected associations among nodes and also achieved better prediction performance than the BNs learned with neighbours thresholds to the computed. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Bayesian Networks learning
Model averaging
Robust structures
Analytical threshold
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