A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Decomposition hybrid algorithms with the recursive framework which recursively decompose the structural task into structural subtasks to reduce computational complexity are employed to learn Bayesian network (BN) structure. Merging rules are commonly adopted as the combination method in the combination step. The direction determination rule of merging rules has problems in using the idea of keeping v-structures unchanged before and after combination to determine directions of edges in the whole structure. It breaks down in one case due to appearances of wrong v-structures, and is hard to operate in practice. Therefore, we adopt a novel approach for direction determination and propose a two-stage combination method. In the first-stage combination method, we determine nodes, links of edges by merging rules and adopt the idea of permutation and combination to determine directions of contradictory edges. In the second-stage combination method, we restrict edges between nodes that do not satisfy the decomposition property and their parent nodes by determining the target domain according to the decomposition property. Simulation experiments on four networks show that the proposed algorithm can obtain BN structure with higher accuracy compared with other algorithms. Finally, the proposed algorithm is applied to the thickening process of gold hydrometallurgy to solve the practical problem.

The Substrate-Independence Theory: Advancing Constructor Theory to Scaffold Substrate Attributes for the Recursive Interaction between Knowledge and Information

The substrate-independence theory utilizes sensemaking techniques to provide cognitively based scaffolds that guide and structure learning. Scaffolds are cognitive abstractions of constraints that relate to information within a system. The substrate-independence theory concentrates on the flow of information as the underlying property of the host system. The substrate-independence theory views social systems as complex adaptive systems capable of repurposing their structure to combat external threats by utilizing constructors and substrates. Constructor theory is used to identify potential construction tasks, the legitimate input and output states that are possible, to map the desired change in the substrate’s attributes. Construction tasks can be mapped in advance for ordered and known environments. Construction tasks may also be mapped in either real-time or post hoc for unordered and complex environments using current sensemaking techniques. Mapping of the construction tasks in real-time becomes part of the landscape, and scaffolds are implemented to aid in achieving the desired state or move to a more manageable environment (e.g., from complex to complicated).

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.

A Bayesian Network Structure Learning Approach to Identify Genes Associated with Stress in Spleens of Chickens

Differences in the expression patterns of genes have been used to measure the effects of non-stress or stress conditions in poultry species. However, the list of genes identified can be extensive and they might be related to several biological systems. Therefore, the aim of this study was to identify a small set of genes closely associated with stress in a poultry animal model, the chicken (Gallus gallus), by reusing and combining data previously published together with bioinformatic analysis and Bayesian networks in a multi-step approach. Two datasets were collected from publicly available repositories and pre-processed. Bioinformatics analyses were performed to identify genes common to both datasets that showed differential expression patterns between non-stress and stress conditions. Bayesian networks were learnt using a Simulated Annealing algorithm implemented in the software Banjo. The structure of the Bayesian network consisted of 16 out of 19 genes in addition to the stress condition. CARD19 displayed a direct relationship with the stress condition, and three other genes, CYGB, BRAT1, and EPN3 were also relevant for the stress condition. The biological functionality of these genes are related to damage, apoptosis, and oxygen provision, and they could potentially be further explored as biomarkers of stress.

Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks

In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis.