Using a Bayesian network to analyze the causal relationship between nodes is
a hot spot. The existing network learning algorithms are mainly
constraint-based and score-based network generation methods. The
constraint-based method is mainly the application of conditional independence
(CI) tests, but the inaccuracy of CI tests in the case of high dimensionality
and small samples has always been a problem for the constraint-based method.
The score-based method uses the scoring function and search strategy to find
the optimal candidate network structure, but the search space increases too
much with the increase of the number of nodes, and the learning efficiency is
very low. This paper presents a new hybrid algorithm, MCME (multiple compound
memory erasing). This method retains the advantages of the first two methods,
solves the shortcomings of the above CI tests, and makes innovations in the
scoring function in the direction discrimination stage. A large number of
experiments show that MCME has better or similar performance than some existing
algorithms.

本文提出了一种新的混合算法 MCME，解决了基于约束的方法中 CI 测试的不准确性和基于得分的方法中搜索空间增加和学习效率低下的问题，并在方向判别阶段中的得分函数方面做出创新。大量实验证明 MCME 的性能优于或类似于一些现有算法。