Learning Bayesian Networks Research Articles

Learning Bayesian Networks for the Prediction of Unfavorable Health Events in Nursing Homes.

This study (This research is funded by Teranga Software and the ANRT CIFRE Grant no2019/1519 for CC.) proposes the use of Bayesian Networks for the prediction of unfavorable health events, and more especially pressure ulcers, in nursing homes. From a database of electronic medical records, we learn an explainable and relevant classifier, which performs better than the scores currently used in nursing homes.

Alleviating the attribute conditional independence and I.I.D. assumptions of averaged one-dependence estimator by double weighting

Learning Bayesian network classifiers (BNCs) from data is NP-hard. Of numerous BNCs, averaged one-dependence estimator (AODE) performs extremely well against more sophisticated newcomers, and its trade-off between bias and variance can be attributed to the independence assumption and i.i.d. assumption, which respectively address the issues of structure complexity and data complexity. To alleviate these assumptions and improve AODE, we propose to apply double weighting, including attribute weighting and model weighting, to finely tune the estimates of conditional probability based on generative learning and joint probability based on discriminative learning, respectively. Instance weighting is introduced to define the information-theoretic metrics for identifying the variation in probability distributions for different data points. This highly scalable learning approach can establish a decision boundary that is specifically tailored to each instance. Our extensive experimental evaluation on 34 datasets from the UCI machine learning repository shows that, attribute weighting and model weighting are complementary although they can work separately. The proposed AODE applying double weighting schema, called DWAODE, is a competitive alternative to other weighting approaches. The experimental results show that DWAODE demonstrates significant advantage in terms of zero–one loss, bias–variance decomposition, RMSE (root mean squared error), Friedman and Nemenyi tests.

ACGAN and BN based method for downhole incident diagnosis during the drilling process with small sample data size

During the drilling process, the complicated geological environment makes drilling operations more difficult as the drilling depth increases, leading to a greater susceptibility to drilling incidents. The parameters obtained from a drilling incident are usually incomplete and the sample size is small, which is difficult to be used for incident analysis. This paper proposes a new method for the diagnosis of downhole drilling incidents. The drilling data is generated based on an Auxiliary Classifier Generative Adversarial Networks (ACGAN) and an incident diagnosis model is built using the Bayesian network (BN). The effectiveness and practicality of the proposed method are proved by the actual case study. Based on historical data, data augmentation is performed using the ACGAN model, and then parameter learning of BN is conducted. The established BN model based on large data samples can be used for the diagnosis of downhole incidents. The precision and F1-score of diagnosis are above 80%. Root cause diagnosis of downhole incidents can be performed by backward inference of Bayesian methods. It can prevent the occurrence of downhole incidents. The results prove the proposed method can diagnose downhole incidents in real time and obtain the causes of downhole incidents.

IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0

Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The constructive impacts of Industry 4.0 are also replicated in Education 4.0. Real-time assessment, irregularity detection, and alert generation are some of the leading necessities of Education 4.0. Conspicuously, this study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0. The proposed framework correspondingly addresses the comparable issues of Industry 4.0. The proposed study (1) recommends the use of IoT, Fog, and Cloud Computing, i.e., IFC technological integration for the implementation of Education 4.0. Subsequently, (2) the Symbolic Aggregation Approximation (SAX), Kalman Filter, and Learning Bayesian Network (LBN) are deployed for data pre-processing and classification. Further, (3) the assessment, irregularity detection, and alert generation are accomplished over SoTL (the set of threshold limits) and the Multi-Layered Bi-Directional Long Short-Term Memory (M-Bi-LSTM)-based predictive model. To substantiate the proposed framework, experimental simulations are implemented. The experimental outcomes substantiate the better performance of the proposed framework, in contrast to the other contemporary technologies deployed for the enactment of Education 4.0.

Optimized structure learning of Bayesian Network for investigating causation of vehicles’ on-road crashes

A vehicle's crash can be seen as a failure of microscopic road transportation system. The causal investigation of vehicles’ crashes has drawn much attention from academia and industry alike, which is of significance to road traffic safety. This study develops a structure learning method to construct Bayesian Network (BN). The BN as generated by the method can comprehensively illustrate the causal relationships between risk contributing features and vehicles’ on-road risky events (i.e. near-crash and crash). The proposed structure learning method has following three advantages: (1). considering multiple categories of features; (2). applying robust feature selection method to improve prediction performance and facilitate the explanation of causation; and (3). making a trade-off between the complexity and interpretability of BN structure. The method is applied on the Second Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) database for case study. The results show that the generated optimal BN achieves satisfactory performances on both structure complexity and prediction accuracy. Besides, as compared to the BN built by the other state-of-the-art structure learning methods, the optimal BN presents superior performance on causal interpretability. Also, by performing causal inferences upon the optimal BN, this study examines and analyzes the contributions of several key features to the risky events. Several interesting findings about the features’ contributions are reported in this paper, which could provide valuable references for road safety engineering in the future.

Bayesian networks elucidate complex genomic landscapes in cancer

Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets.

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. "Deleting, reversing, moving, and inserting" is an approach to finding the best answer to the proposition of the problem in the algorithm. In the Enhanced Surface Water Searching Technique, most of the hunting for water is done by elephants during dry seasons. Pigeon Optimization, Simulated Annealing, Greedy Search, and the BDeu metrics are being reviewed in combination to evaluate all these strategies being used to solve this problem. They subjected different data sets to the uncertainty matrix in an investigation to find out which of these approaches performed best. According to the evaluation data, the algorithm shows stronger results and delivers better points. Additionally, this article also represents the structure of the learning processes for Bayesian Networks as well

Wideband Multitarget Tracking Based on Dynamic Bayesian Network Learning in an Acoustic Sensor Array Network

The multi-target tracking (MTT) based on distributed fusion methods in an acoustic sensor array network (ASAN) is limited by the performance of measurement parameter estimators, such as the received signal strength (RSS) and the direction-of-arrival (DOA). For measurement parameters with low accuracy and resolution, the MTT may fail in subsequent steps, e.g., data association, because the loss of upstream information cannot be made up by downstream processing. Thus, we propose a new wideband MTT algorithm based on dynamic Bayesian network (DBN), which treats the ASAN as an overall extended array, and directly estimates the target states from the raw acoustic data. The DBN fuses the near-field model, the acoustic propagation model and the motion model. These sub-models can be optimized by each other, improving the final estimation. Also, for each sub-band, target signals and the precision parameters of the sensor noise are treated as hidden random variables. Based on this, the weight of each sub-band can be automatically adjusted according to the accurate hidden variables. Besides, the optimization problem of the posterior probability is transformed into a graphical model learning problem. Moreover, for non-conjugate models, a novel algorithm based on Laplace approximations (LAs) with Newton’s method (NM) is developed, i.e., DBN-LA-NM, bypassing data association. In addition, the corresponding CRLB and convergence conditions are derived. The numerical simulation results show that the proposed algorithm outperforms existing MTTs based on the near-field model in terms of accuracy, convergence and computational complexity. Field experiments further verify the feasibility of the proposed algorithm.

Expert Knowledge-Driven Bayesian Network Modeling for Marine Disaster Assessment Under the Small Sample Condition

Risk assessment and management of marine disasters are the prerequisite of ocean exploitation and utilization. Marine disaster assessment is a complicated system engineering with high non-linearity and uncertainty. To deal with the problem, Bayesian network (BN) has become a powerful model used for disaster assessment due to its capability of expressing complex relationships and reasoning with uncertainty. However, scarce data sets and case samples of marine disasters pose an obstacle to BN modeling, particularly for structure and parameter learning. In our research, we combine expert knowledge with small sample to propose a new BN-based assessment model. Expert knowledge is regularly expressed and quantitatively incorporated into BN learning with DS evidence theory. Then, the genetic algorithm is adopted to search the optimal network parameters. Comparative experiments show that the new model has a better assessment accuracy (91.03%) than BPNN (61.34%) and SVM (70.67%), especially with small samples. The proposed model achieves the risk assessment of marine disasters under the small sample condition, providing the technical support for marine disaster prevention and mitigation.

A novel discrete firefly algorithm for Bayesian network structure learning

As an effective tool for the representation and reasoning of uncertain theories, Bayesian networks are widely used in various fields of artificial intelligence. However, learning the optimal Bayesian network structure is an NP-hard problem. The score-and-search approach is a common method for solving the problems associated with Bayesian network structure learning. This paper presents a novel method for learning the structure of a Bayesian network using a discrete firefly optimization algorithm, which has been successfully applied to solve various optimization problems. In the proposed algorithm, each firefly moves in a discrete space based on a redefined movement strategy. Then, the mutation operator is employed to prevent the algorithm from stopping prematurely and falling into a local optimum. Finally, a local optimizer is used to enhance the exploitation ability of the firefly to obtain the best feasible solution. We compared the proposed algorithm with state-of-the-art algorithms on well-known benchmark networks. The experimental results show that the proposed algorithm has better convergence accuracy and higher scores in most cases, compared to other algorithms, indicating that the proposed algorithm can be used as an effective and feasible method for learning Bayesian network structures.

A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data

The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples.

Efficient Sampling and Structure Learning of Bayesian Networks

Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high-dimensional data, and even to facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) is highly challenging mainly due to the vast number of possible networks in combination with the acyclicity constraint. Efforts have focused on two fronts: constraint-based methods that perform conditional independence tests to exclude edges and score and search approaches which explore the DAG space with greedy or MCMC schemes. Here, we synthesize these two fields in a novel hybrid method which reduces the complexity of MCMC approaches to that of a constraint-based method. Individual steps in the MCMC scheme only require simple table lookups so that very long chains can be efficiently obtained. Furthermore, the scheme includes an iterative procedure to correct for errors from the conditional independence tests. The algorithm offers markedly superior performance to alternatives, particularly because it also offers the possibility to sample DAGs from their posterior distribution, enabling full Bayesian model averaging for much larger Bayesian networks. Supplementary materials for this article are available online.

Dynamic embeddings for efficient parameter learning of Bayesian network with multiple latent variables

Latent variables (LVs), representing the unobservable abstract concepts, such as patient disease and customer credit, play an important role in the simplification of network structure and improving the interpretability of Bayesian network (BN). However, LVs incorporated into BN lead to missing probability parameters due to the missing observation. As the classic method for parameter estimation for the situation with LVs, the expectation–maximization (EM) suffers high complexity and slow convergence. To this end, we propose dynamic embeddings for parameter learning of BN with LVs. Firstly, we reconstruct the E-step of EM and propose to use the dynamic embeddings to calculate the weights of fractional samples that could reduce the computational complexity of parameter learning. Secondly, we propose to construct the point mutual information (PMI) matrix to represent directed weighted graphs (DWGs) transformed from the updated parameters. Thirdly, the incremental singular value decomposition (SVD) is adopted to generate dynamic embeddings while capturing the updated parameters and preserving BN’s graphical structure. Experimental results show that our proposed methods are efficient and effective. On real-world BNs, the efficiency, convergence and accuracy of our method outperform those of the state-of-the-art methods for parameter learning of BN with multiple LVs.

Increasing Uptake of Social Distancing During COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions

Social distancing emerged as one of the early critical nonpharmaceutical interventions to fight the spread of COVID-19. However, in the United States, this behavior was not universally adopted. Understanding COVID-relevant behaviors is crucial to helping policymakers develop targeted, actionable interventions that meet the urgent needs of a global pandemic in a precision public health approach—that is, getting the right intervention to the right person, at the right time and place. In this article, we demonstrate how using a toolbox that includes a comprehensive data collection design framework, machine learning tools to do causal discovery (i.e., structural learning of a causal Bayesian network), and clustering analysis can help disentangle the intertwined hierarchy of drivers of social distancing to design targeted, actionable interventions in a precision public health application. We integrated several machine learning techniques to generate insights from a nationally representative social distancing survey of 2,500 U.S. respondents, conducted in March 2020. This approach goes beyond measuring the correlates of social distancing intentions and behavior and narrows in on the potential causal drivers of social distancing. Our approach identifies the factors that social distancing directly and conditionally depends upon: higher financial security, higher information-seeking, and higher worry about the coronavirus, as well as other upstream factors. We also identify four population segments to help target interventions: “Worriers,” “Rule-followers,” “Financially constrained,” and “Skeptics.” Two subgroups in particular, the “Skeptics” and the “Financially constrained,” had low uptake of social distancing, but would require different targeted messages to increase social distancing behavior. Taken together, these results demonstrate how machine learning techniques can help prioritize messages most effective for matched population targets, increasing desirable outcomes while potentially saving resources.

Structure learning of bayesian network using swarm intelligent algorithm: a review

Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. “Deleting, reversing, moving, and inserting” is an approach to finding the best answer to the proposition of problem in the algorithm. In the Enhanced Surface Water Searching Technique, mostly, the hunt for water is done by elephants during dry seasons, It is Pigeon Optimization, Simulated Annealing, Greedy search, and the BDeu metrics being reviewed in combination to evaluate all these strategies being used in order to solve this problem. They subjected different data sets to the uncertainty matrix in an investigation to find out which of these approaches performed best. According to evaluation data, the algorithm shows stronger results and delivers better points. Additionally, this article also represents the structure learning processes for Bayesian Network as well.

BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes

Learning a Bayesian network is an NP-hard problem and with an increase in the number of nodes, classical algorithms for learning the structure of Bayesian networks become inefficient. In recent years, some methods and algorithms for learning Bayesian networks with a high number of nodes (more than 50) were developed. But these solutions have their disadvantages, for instance, they only operate one type of data (discrete or continuous) or their algorithm has been created to meet a specific nature of data (medical, social, etc.). The article presents a BigBraveBN algorithm for learning large Bayesian Networks with a high number of nodes (over 100). The algorithm utilizes the Brave coefficient that measures the mutual occurrence of instances in several groups. To form these groups, we use the method of nearest neighbours based on the Mutual information (MI) measure. In the experimental part of the article, we compare the performance of BigBraveBN to other existing solutions on multiple data sets both discrete and continuous. The experimental part also represents tests on real data. The aforementioned experimental results demonstrate the efficiency of the BigBraveBN algorithm in structure learning of Bayesian Networks.

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background: High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions. Objective: We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype. Method: Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge. Results : Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches. Conclusion: OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

Structure Learning of Bayesian Networks Based on Causal Direction

Structure Learning of Bayesian Networks Based on Causal Direction

Simple Bayesian Gene Network Learning in Populus Drought Transcriptome Data

Constructing a sensibly functional gene interaction network is highly appealing for better understanding system-level biological processes governing various Populus traits. Bayesian Network (BN) learning provides an elegant and compact statistical approach for modeling causal gene-gene relationships in microarray data. Therefore, it could come with the illumination of functional molecular playing in Biology Systems. In the present study, different forms of gene Bayesian networks were detected on Populus cellular transcriptome data. Markov blankets would likely be emerging at every possible gene regulatory Bayesian network level. Results showed that PtpAffx.1257.4.S1_a_at,1.0 hypothetical protein is the most important in its possible regulatory program. This paper illustrates that the gene network regulatory inference is possible to encapsulate within a single BN model. Therefore, such a BN model can serve as a promising training tool for Populus gene expression data for better future experimental scenarios. Bangladesh J. Bot. 50(4): 1077-1086, 2021 (December)

Exact Learning Augmented Naive Bayes Classifier.

Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method.