Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

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.

Traffic state prediction based on stochastic repetitive hill climbing method

A reasonable structure of traffic state network is a prerequisite for traffic state prediction. In order to overcome the shortcomings of the hill climbing method, a traffic state prediction method based on the random repetitive hill climbing method is proposed. A multi-network structure is obtained by iteratively running the hill-climbing method on the randomly generated directed acyclic graph; the node and directed edge selection criteria in the optimal Bayesian network structure are determined by defining the confidence degree of directed edges and calculating the confidence threshold; using the optimal Bayesian network structure, four traffic states, such as smooth, smooth, congested and blocked, are predicted and evaluated comprehensively. The analysis results show that the overall accuracy of the method for traffic state prediction exceeds 85\% when only two variables such as time of day and holiday are selected, which can provide effective methods and data support for highway operation state monitoring and early warning and decision analysis.

A Mobile Bayesian Network Structure Learning Method Using Genetic Incremental K2 Algorithm and Random Attribute Order Technology

The application of existing datasets to construct a probabilistic network has always been the primary research focus for mobile Bayesian networks, particularly when the dataset size is large. In this study, we improve the K2 algorithm. First, we relax the K2 algorithm requirements for node order and generate the node order randomly to obtain the best result in multiple random node order. Second, a genetic incremental K2 learning method is used to learn the Bayesian network structure. The training dataset is divided into two groups, and the standard K2 algorithm is used to find the optimal value for the first set of training data; simultaneously, three similar suboptimal values are recorded. To avoid falling into the local optimum, these four optimal values are mutated into a new genetic optimal value. When the second set of training data is used, only the best Bayesian network structure within the five abovementioned optimal values is identified. The experimental results indicate that the genetic incremental K2 algorithm based on random attribute order achieves higher computational efficiency and accuracy than the standard algorithm. The new algorithm is especially suitable for building Bayesian network structures in cases where the dataset and number of nodes are large.

Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation

This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area of artificial intelligence (AI) methods. Ultimately, two models of damage risk assessment were built using the Naive Bayes classifier (NBC) and Bayesian Networks (BN). The first model was used to compare results obtained using the more computationally advanced Bayesian network methodology. In the case of the Bayesian network, the unknown Directed Acyclic Graph (DAG) structure was extracted using Chow-Liu’s Tree Augmented Naive Bayes (TAN-CL) algorithm. Thus, one of the methods involving Bayesian Network Structure Learning from data (BNSL) was implemented. The application of this approach represents a novel scientific contribution in the interdisciplinary field of mining and civil engineering. The models created were verified with respect to quality of fit to observed data and generalization properties. The connections in the Bayesian network structure obtained were also verified with respect to the observed relations occurring in engineering practice concerning the assessment of the damage intensity to masonry buildings in mining areas. This allowed evaluation of the model and justified the utility of the conducted research in the field of protection of mining areas. The possibility of universal application of the Bayesian network, both in the case of damage prediction and diagnosis of its potential causes, was also pointed out.

Constraint-Based Bayesian Network Structure Learning using Uncertain Experts’ Knowledge

Exploiting experts' knowledge can significantly increase the quality of the Bayesian network (BN) structures produced by learning algorithms. However, in practice, experts may not be 100% confident about the opinions they provide. Worst, the latter can also be conflicting. Including such specific knowledge in learning algorithms is therefore complex. In the literature, there exist a few score-based algorithms that can exploit both data and the knowledge about the existence/absence of arcs in the BN. But, as far as we know, no constraint-based learning algorithm is capable of exploiting such knowledge. In this paper, we fill this gap by introducing the mathematical foundations for new independence tests including this kind of information. We provide a new constraint-based algorithm relying on these tests as well as experiments that highlight the robustness of our method and its benefits compared to other constraint-based learning algorithms.