Bayesian Network Structure Research Articles

Fault diagnosis method of rolling bearing of BN ball mill based on AESL-GA

Aiming at the shortcomings of incomplete and inaccurate knowledge-based Bayesian network (BN) construction methods, a BN structure construction method based on knowledge guidance and data mining is proposed. Aiming at the problem of inaccurate single signal fault diagnosis results and the uncertainty problem in fault information, the current signal and the vibration signal are fused to establish the feature nodes of the BN network, and the fault feature parameters of the two signals are extracted respectively. The feature basket is selected using the discrimination index method and used as the node of the BN structure feature layer. The initial BN structure constructed by expert knowledge is combined with the adaptive elite structure genetic algorithm (AESL-GA) for structural optimization. By adaptively restricting the search space in the evolution process, reducing the number of free parameters, improving its global search ability, and obtaining the optimal BN structure. The method is verified by the measured data of the ball mill rolling bearing of Jinchuan Company and the Paderborn University data set, which proves the effectiveness of the proposed method.

A Bayesian network approach to assess the ecosystem integrity of mangroves in Tampamachoco, Veracruz, Mexico

Given the alarming rates of mangrove forest loss, resource managers must count on information regarding the condition of the mangrove forests. We propose a Bayesian network (BN) to assess mangrove forests' ecosystem integrity (EI) to support a mangrove monitoring system in Mexico. This approach allowed us to infer the system's condition based on variables on forest structure and function. We defined the BN structure based on informal interviews with specialists on vegetation and coastal geomorphology. We applied the expectation-maximization learning algorithm to train the model. Data from plots in two mangrove areas of an Avicennia germinans forest, defined based on their undisturbed and disturbed conditions, were used as training datasets. We applied sensitivity analysis to determine the degree of influence of each model variable. We evaluated the prediction capacity of the BN with a k-fold cross-validation (the process is repeated five times, starting from the database in 2 parts). The variables selected for the model were the Holdridge complexity index (HCI, Holdridge et al., 1971), Leaf area index (LAI), litter production (g/month/m2), leaf C, N and P concentration (%), and leaf N:P ratio. The most critical variable to infer mangrove condition was leaf N:P (Variance reduction = 11%), followed by forest structure variables HCI and LAI (Variance reduction > 5%). The cross-validation to test the model resulted in a minimum square error of 0.3, which indicates a reasonable capacity to predict the condition of mangrove integrity. The BN constructed can diagnose the integrity of a monospecific mangrove forest with acceptable precision, considering the environmental factors that define the forest structure and functioning locally. We then asked the experts to review and modify the model to apply to multispecies mangrove ecosystems and environmental contexts.

Assessing Credibility in Bayesian Networks Structure Learning.

Learning Bayesian networks from data aims to create a Directed Acyclic Graph that encodes significant statistical relationships between variables and their joint probability distributions. However, when using real-world data with limited knowledge of the original dynamical system, it is challenging to determine if the learned DAG accurately reflects the underlying relationships, especially when the data come from multiple independent sources. This paper describes a methodology capable of assessing the credible interval for the existence and direction of each edge within Bayesian networks learned from data, without previous knowledge of the underlying dynamical system. It offers several advantages over classical methods, such as data fusion from multiple sources, identification of latent variables, and extraction of the most prominent edges with their respective credible interval. The method is evaluated using simulated datasets of various sizes and a real use case. Our approach was verified to achieve results comparable to the most recent studies in the field, while providing more information on the model's credibility.

Constructing the graphical structure of expert-based Bayesian networks in the context of software engineering: A systematic mapping study

Context:In scenarios where data availability issues hinder the applications of statistical causal modeling in software engineering (SE), Bayesian networks (BNs) have been widely used due to their flexibility in incorporating expert knowledge. However, the general understanding of how the graphical structure, i.e., the directed acyclic graph (DAG), of these models is built from domain experts is still insufficient. Objective:This study aims to characterize the SE landscape of constructing the graphical structure of BNs, including their potential for causal modeling. Method:We conducted a systematic mapping study employing a hybrid search strategy that combines a database search with parallel backward and forward snowballing. Results:Our mapping included a total of 106 studies. Different methods are commonly combined to construct expert-based BN structures. These methods span across data gathering & analysis (e.g., interviews, focus groups, literature research, grounded theory, and statistical analysis) and reasoning mechanisms (e.g., using idioms combined with the adoption of lifecycle models, risk-centric modeling, and other frameworks to guide BN construction). We found a lack of consensus regarding validation procedures, particularly critical when modeling cause–effect relationships from knowledge. Additionally, expert-based BNs are mainly applied at the tactical level to address problems related to software engineering management and software quality. Challenges in creating expert-based structures include validation procedures, experts’ availability, expertise level, and structure complexity handling. Key recommendations involve empirical validation, participatory involvement, and balance between adaptation to organizational constraints and model construction requirements. Conclusion:The construction of expert-based BN structures in SE varies in rigor, with some methods being systematic while others appear ad hoc. To enhance BN application, reducing expert knowledge subjectivity, enhancing methodological rigor, and clearly articulating the construction rationale is essential. Addressing these challenges is crucial for improving the reliability of causal inferences drawn from these models, ultimately leading to better-informed decisions in SE practices.

An evaluation method of construction reliability of cable system of cable-stayed bridge based on Bayesian network

PurposeAs an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.Design/methodology/approachFirst of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.FindingsThe feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.Originality/valueThis paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

Enhancing the synergy governance capability of urban communities based on Bayesian network: A case of Jinan city

The rapid pace of urbanization has brought about a plethora of challenges for urban communities, prompting the advocacy of collaborative governance as a means to tackle management issues effectively. However, fostering strong collaboration among multiple governance entities remains a significant hurdle in urban communities governance. This study addresses this challenge by exploring the complex structure of urban communities' collaborative governance systems, with a focus on enhancing collaborative governance capability. Drawing on Bayesian network (BN) theory and utilizing the case of Jinan, this article investigates the intrinsic mechanisms of achieving collaboration in urban communities. This study establishes a Bayesian network model for collaborative governance of urban communities, providing a visual representation of the intricate interactions among multiple governance entities. We employ a data-knowledge-driven approach for BN structure learning. This approach tackles issues such as the lack of cyclic dependency capability and the impact of small datasets on model accuracy. Furthermore, we analyze the collaborative mechanism through Bayesian reasoning. The findings underscore the pivotal entities and five key factors that significantly influence collaborative governance capability. Lastly, policy implications are drawn to enhance the collaborative governance capability of urban communities.

Enhancing Periodontal Treatment Through the Integration of Deep Learning-Based Detection with Bayesian Network Models.

This study incorporated deep learning for periodontal disease detection into a Bayesian network (BN) clinical decision support model for comprehensive periodontal care. BN structure and probabilities were based on clinical data and Faster R-CNN-detected radiographic images. Receiver operating characteristic curve analysis confirmed the model's high accuracy in treatment plan recommendations.

Bayesian network structure learning by dynamic programming algorithm based on node block sequence constraints

Bayesian network structure learning by dynamic programming algorithm based on node block sequence constraints

Organizational and Individual Contributing Factors to Safety Climate in Healthcare Industries - Bayesian Network Predictive Modeling Approach.

The current study aims to identify individual and joint drivers that significantly influence the safety climate in healthcare industries by using Bayesian Network simulations for an in-depth analysis. Survey data were collected from 452 employees from two branches of one hospital in China for a study about workplace safety. The original English surveys were translated into Chinese using the back-translation procedure recommended by Brislin. Employees were asked to complete two online surveys with one month in between each administration. The sample was 42% doctors and 58% nurses. A Bayesian Network (BN) model, based on theory, was updated and complemented with expert knowledge. A graphical model based on expert knowledge and data-driven machine learning approaches was used to refine the BN structure, representing interrelationships among all studied variables. The BN model was employed to identify the best key drivers and joint strategies for safety climate improvement. The Bayesian Network model demonstrated a good overall fit. The Euclidean distance metric was used to assess the influence between connected variables, with interpersonal trust and locus of control having the strongest independent effects on safety climate among the five contributing factors. Joint strategies, particularly joint optimization of error disclosure culture and interpersonal trust, as well as error disclosure culture and self-efficacy, were most effective in promoting a safe climate. The findings suggest that hospital safety climate can be improved by providing a psychologically safe error disclosure culture and enhancing interpersonal trust among employees and their self-efficacy.

How to assess conditions for the acceptance of climate change adaptation measures by applying implementation probability Bayesian Networks in participatory processes

Climate change adaptation measures are best identified participatorily, yet their implementation poses challenges. While Bayesian Network (BN) modeling has been widely used to assess how adaptation measures mitigate risks, we present how to develop, in a participatory process, an innovative BN type that quantifies the implementation probability of adaptation measures by considering conditions for actors’ acceptance as well as cultural worldviews. The BN structure was derived from participatorily identified causal networks, while the conditional probability tables were straightforwardly developed with stakeholder-assigned weights. Sensitivity analysis shows how BN structure and parameters influence the BN results. We found that our approach achieves knowledge integration and learning without overwhelming stakeholders with technical details. As BNs enable exploring scenarios, stakeholders learn that many plausible futures exist. Integrating our approach in participatory adaptation processes contributes to identifying the best combinations of implementation actions, reducing the “know-do gap” in local adaptation challenges.

A stage-driven construction algorithm of undirected independence graph for Bayesian network structure learning

A stage-driven construction algorithm of undirected independence graph for Bayesian network structure learning

Updating strategy of safe operation control model for dense medium coal preparation process based on Bayesian network and incremental learning

AbstractThe effectiveness of control decisions provided by the safe operation control model for the dense medium coal preparation process may decline due to its inability to adapt to changing working conditions. To address this issue, this paper investigates a safe operation control model update strategy based on Bayesian network and incremental learning. This strategy can update the model structure and parameters according to different conditions, ensuring the effectiveness of the updated model. Considering that the old model has effective information to explain the new working conditions, the Bayesian network structure update learning method based on incremental learning is proposed. This method retains the components of the old model that can describe the joint probability distribution of the sampled data under the new working conditions while updating the remaining structure. This approach improves the efficiency of model updating. The simulation results show that the updated model obtained by the proposed method can effectively deal with new abnormal conditions.

A hybrid approach integrating case mining (CM) and the Copula Bayesian Network (CBN) for accident causation probabilistic reasoning of building construction collapses

Construction collapses represent a prevalent type of structural failure in the building industry, involving multiple causal factors. Most current studies on these causal factors and their relationships are derived from expert knowledge and rely primarily on singular probabilistic estimates derived from expert input to predict the likelihood of accidents using Bayesian networks. However, fluctuations in expert knowledge can compromise the stability of Bayesian network structures, thus failing to accurately capture the uncertainty associated with construction collapses. To fill these gaps, a hybrid approach, integrating case mining (CM) and a Copula Bayesian Network (CBN), is proposed to infer the causation probability of construction collapse. To construct a stable Bayesian network structure, the CM method is used to extract the causal factors and their relationships from accident investigation reports. The Gaussian copula function and different marginal distribution functions are introduced to describe the probability distribution of the occurrence of causal factors. Correlation analysis, forward analysis and backward analysis are used to quantify the risk propagation relationships among the causal factors. The findings indicate that (1) construction collapses predominantly occur between 9:00-10:00 and 14:00-15:00 and (2) illegal construction, lack of administrative supervision and non-implementation of construction schemes are critical factors contributing to these incidents.

System-level operational cyber risks identification in industrial control systems

ABSTRACT In Industrial Control Systems (ICS), where complex interdependencies abound, cyber incidents can have far-reaching consequences. Dependency modelling, a valuable technique for assessing cyber risks, aims to decipher relationships among variables. However, its effectiveness is often hampered by limited data exposure, hindering the analysis of direct and indirect impacts. We present a unique method that transforms dependency modelling data into a Bayesian Network (BN) structure and leverages causality and reasoning to extract inferences from seemingly unrelated events. Using operational ICS data, we confirm our method enables stakeholders to make better decisions about system security, stability, and reliability.

A novel bayesian network-based ensemble classifier chains for multi-label classification

In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.

A Hybrid Method: Resolving the Impact of Variable Ordering in Bayesian Network Structure Learning

A Hybrid Method: Resolving the Impact of Variable Ordering in Bayesian Network Structure Learning

Bayesian network structure learning using scatter search

Learning the structure of Bayesian networks (BNs) from data is an NP-hard problem as the solution space grows super-exponentially with the number of nodes. Many algorithms have been developed to efficiently find the best structures, with score-based algorithms being those that use heuristics or metaheuristics to explore potential structures in the search space. This paper proposes a new score-based algorithm that relies on a well-known metaheuristic called scatter search, which, to the best of our knowledge, has not been used in learning BN structure. The core of scatter search is to maintain a reference set that stores both high-quality and diverse solutions, thereby continuously tracking and improving the high-quality solutions, while exploring different search directions indicated by the diverse solutions. By incorporating a distance metric in the learning process, the exploration can be more systematic than purely random, as is often the case in most existing algorithms. The effectiveness and efficiency of the proposed algorithm are evaluated through computational experiments. In addition to learning higher-score structures, the results show that scatter search provides a higher degree of robustness compared with benchmark algorithms.

An application of the Bayesian network model based on the EN-ESL-GA algorithm: Exploring the predictors of heart disease in middle-aged and elderly people in China.

The morbidity and mortality of heart disease are increasing in middle-aged and elderly people in China. It is necessary to explore relationships and interactive associations between heart disease and its risk factors in order to prevent heart disease. To establish a Bayesian network model of heart disease and its influencing factors in middle-aged and elderly people in China, and explore the applicability of the elite-based structure learner using genetic algorithm based on ensemble learning (EN-ESL-GA) algorithm in etiology analysis and disease prediction. Based on the 2013 national tracking survey data from China Health and Retirement Longitudinal Study (CHARLS) database, EN-ESL-GA algorithm was used to learn the Bayesian network structure. Then we input the data and the learned network structure into the Netica software for parameter learning and inference analysis. The Bayesian network model based on the EN-ESL-GAalgorithm can effectively excavate the complex network relationships and interactive associations between heart disease and its risk factors in middle-aged and elderly people in China. The Bayesian network model based on the EN-ESL-GA algorithm has good applicability and application prospect in the prediction of diseases prevalence risk.

The impact of variable ordering on Bayesian network structure learning

Causal Bayesian Networks (CBNs) provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are becoming increasingly important. In the literature, the validity of these algorithms is often tested for sensitivity over varying sample sizes, hyper-parameters, and occasionally objective functions, but the effect of the order in which the variables are read from data is rarely quantified. We show that many commonly-used algorithms, both established and state-of-the-art, are more sensitive to variable ordering than these other factors when learning CBNs from discrete variables. This effect is strongest in hill-climbing and its variants where we explain how it arises, but extends to hybrid, and to a lesser-extent, constraint-based algorithms. Because the variable ordering is arbitrary, any significant effect it has on learnt graph accuracy is concerning, and raises questions about the validity of both many older and more recent results produced by these algorithms in practical applications and their rankings in performance evaluations.

Bayesian network structure learning with a new ensemble weights and edge constraints setting mechanism

Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods.