Abstract
Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level α is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy.
Highlights
In recent years, big data, machine learning, and deep learning have become hot spots of common concern in academia and industry, such as computer science, medicine, statistics, economy, and social sciences [1,2,3,4,5]
Yuan et al [36] proposed to apply an improved A∗ algorithm in the dynamic programming space to reduce the complexity of space and time; Liu [37] proposed a new optimal path selection based on a hybrid improved A∗ algorithm and reinforcement learning method and obtained stable and efficient application effects in the optimal path selection of intelligent driving vehicles; Tan et al [38] proposed a bidirectional heuristic search algorithm (BiHS) based on one-way heuristic search, and the results showed the BiHS algorithm is more efficient than the one-way heuristic search algorithm
It can be seen that our algorithm reduces its time consumption and complexity by reducing the number of scoring times. e algorithm accuracy is subject to the significance level α, but not affected by the horizontal pruning with t
Summary
Big data, machine learning, and deep learning have become hot spots of common concern in academia and industry, such as computer science, medicine, statistics, economy, and social sciences [1,2,3,4,5]. Artificial intelligence scholars proposed at the annual conference of artificial intelligence that future artificial intelligence research should be oriented to uncertain environments and oriented to human-like mechanisms, and focus on dealing with complex problems and limited data learning problems. E “black box” design of the deep neural network model makes it difficult to explain its internal operating mechanism, and training the network requires a large number of labeled samples. E methods that can deal with uncertainty and combine domain knowledge to model complex problems, such as fuzzy neural network [7, 8], Bayesian network [9], and DS evidence theory [10, 11], have attracted people’s attention again. The Bayesian network has many advantages that other modeling methods do not have. With its rigorous mathematical foundation, graphical topology that is easy to understand intuitively, and the natural expression of real problems, it has become the powerful tool for uncertain information processing and posterior probabilistic reasoning, which has been widely used in genetic analysis [12], medical diagnosis [13], reliability analysis [14], and threat assessment [15]
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