基于评分缓存的节点序空间下 BN 结构学习

Aiming at the problem that large-scale Bayesian network structure learning falls into local optima easily, an iterative local search algorithm in node ordering space is proposed. During the local search step, the selective insertion operator based on score cache and the tolerance strategy lor suboptimal Solutions are designed. The adaptive longitudinal insertion neighborhood domain is evaluated to overcome the limited neighborhood domain problem caused by blind search. During the iterative restart step, the conversion mechanism ol equivalent dass structure and depth-first search (DFS) is adopted to prevent score degradation problem caused by random disturbances. Alter verilying the effectiveness of the search and iterative algorithms through fusion experiments, the experimental results show that compared with existing mainstream methods, the iterative local search algorithm can learn large-scale network structures accurately.