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Parallel cycle-based branch-and-bound method for Bayesian network learning
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DOI：10.1007/s10044-019-00815-1.png)
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Bayesian networks (BNs) are one of the most commonly used models for representing uncertainty in medical diagnosis. Learning the exact structure of a BN is a challenging problem. This paper proposes a multi-threaded branch-and-bound (B&B) method, called parallel cycle-based branch-and-bound (parallel CB-B&B). On the one hand, CB-B&B improves the standard B&B method by leveraging two heuristics, namely the branching strategy and the bounding operators; on the other hand, the learning procedure is alleviated by executing CB-B&B over a set of parallel processors. In comparison with conventional exact structure learning approaches for BN, the obtained results demonstrate that the proposed CB-B&B is efficient. On average, it produces the exact structure for BN three times faster than the standard B&B version. We also present simulations on parallel CB-B&B which show a significant gain in terms of execution time.
Keyword:
Medical diagnosis
Bayesian network
Structure learning
Branch-and-bound
Optimization
Cycle-based
Parallel computing
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