用外记忆前沿宽度优先分支定界搜索改进最优贝叶斯网络学习的Scalability
Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search
摘要：
以前的工作表明，学习贝叶斯网络的最佳结构的问题可以用公式表示为图中的最短路径查找问题，并使用A* 搜索来解决。在本文中，我们提高了这种方法的可扩展性，通过开发一个内存有效的启发式搜索算法学习的贝叶斯网络的结构。而不是使用A*，我们提出了一个边界宽度优先的分支和边界搜索，利用这个问题的搜索图的分层结构，使不超过两层的图形，加上解决方案的重建信息，需要存储在内存中的时间。为了进一步提高可扩展性，该算法将大部分图存储在外部存储器中，例如硬盘，当它不适合RAM时。实验结果表明，所得到的算法解决了显着更大的问题比目前的最先进的。
英文摘要：
Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memory-efficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadth-first branch and bound search that leverages the layered structure of the search graph of this problem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithm stores most of the graph in external memory, such as hard disk, when it does not fit in RAM. Experimental results show that the resulting algorithm solves significantly larger problems than the current state of the art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
