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Parallel cycle-based branch-and-bound method for Bayesian network learning

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Abstract

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.

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Authors and Affiliations

  1. University of Tlemcen, Tlemcen, Algeria

    Youcef Benmouna & Med Amine Chikh

  2. University of Mons, Mons, Belgium

    Mohand Said Mezmaz & Said Mahmoudi

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  1. Youcef Benmouna
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  2. Mohand Said Mezmaz
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  3. Said Mahmoudi
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  4. Med Amine Chikh
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Correspondence to Youcef Benmouna.

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Benmouna, Y., Mezmaz, M.S., Mahmoudi, S. et al. Parallel cycle-based branch-and-bound method for Bayesian network learning. Pattern Anal Applic 23, 897–911 (2020). https://doi.org/10.1007/s10044-019-00815-1

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