Network Structure Learning Algorithm Research Articles

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance.

Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.

Finding community structure in Bayesian networks by heuristic K-standard deviation method

When constructing a Bayesian network for high-dimensional data, due to the complex relationships among distinct nodes, the difficulty in detecting the community structure will directly restrict the feasibility of the divide-and-conquer learning algorithm. This study attempts to solve this problem from the shortest path perspective and proposes the heuristic K-standard deviation algorithm. Firstly, we design a novel heuristic function and drive the A* algorithm to find the shortest paths between nodes in the Bayesian network. In addition, the new heuristic function is theoretically proved to be admissible and consistent. Experiments on different synthetic datasets and benchmark Bayesian networks verify that the proposed heuristic K-standard deviation algorithm generally gets better clustering performance than other representative algorithms and improves the efficiency and accuracy of the conventional Bayesian network structure learning algorithms, especially for high-dimensional data.

Bayesian Network structure learning algorithm for highly missing and non imputable data: Application to breast cancer radiotherapy data

It is not uncommon for real-life data produced in healthcare to have a higher proportion of missing data than in other scopes. To take into account these missing data, imputation is not always desired by healthcare experts, and complete case analysis can lead to a significant loss of data. The algorithm proposed here, allows the learning of Bayesian Network graphs when both imputation and complete case analysis are not possible. The learning process is based on a set of local bootstrap learnings performed on complete sub-datasets which are then aggregated and locally optimized. This learning method presents competitive results compared to other structure learning algorithms, whatever the mechanism of missing data.

Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data.

Advocating the Use of Bayesian Network in Analyzing the Modes of Occurrence of Elements in Coal.

Modes of occurrence of elements in coal are important because they can be used not only to understand the origin of inorganic components in coal but also to determine the impact on the environment and human health and the deposition process of coal seams as well. Statistical analysis is one of the commonly used indirect methods used to analyze the modes of occurrence of elements in coal, among which hierarchical clustering is widely used. However, hierarchical clustering may lead to misleading results due to its limitation that it focuses on the clusters of elements rather than a single element. To tackle this issue, we use the first part of a well-known Bayesian network structure learning algorithm, i.e., Peter-Clark (PC) algorithm, to explore the relationships of the coal elemental data and then infer modes of occurrence of elements in coal. A data set containing 95 Late Paleozoic coal samples from the Datanhao and Adaohai mines in Inner Mongolia, China, is used for the performance evaluation. Analytical results show that many instructive and surprising insights can be concluded from the first part of the PC algorithm. Compared with the hierarchical clustering algorithm, the first part of the PC algorithm demonstrates superiority in analyzing the modes of occurrence of elements in coal.

An improved hybrid structure learning strategy for Bayesian networks based on ensemble learning

In the application of Bayesian networks to solve practical problems, it is likely to encounter the situation that the data set is expensive and difficult to obtain in large quantities and the small data set is easy to cause the inaccuracy of Bayesian network (BN) scoring functions, which affects the BN optimization results. Therefore, how to better learn Bayesian network structures under a small data set is an important problem we need to pay attention to and solve. This paper introduces the idea of parallel ensemble learning and proposes a new hybrid Bayesian network structure learning algorithm. The algorithm adopts the elite-based structure learner using genetic algorithm (ESL-GA) as the base learner. Firstly, the adjacency matrices of the network structures learned by ESL-GA are weighted and averaged. Then, according to the preset threshold, the edges between variables with weak dependence are filtered to obtain a fusion matrix. Finally, the fusion matrix is modified as the adjacency matrix of the integrated Bayesian network so as to obtain the final Bayesian network structure. Comparative experiments on the standard Bayesian network data sets show that the accuracy and reliability of the proposed algorithm are significantly better than other algorithms.

A hybrid approach for forecasting bitcoin series

Bitcoin price prediction is a substantial challenge for cryptocurrency investors. This study offers an innovative scheme to predict Bitcoin returns and volatilities using a hybrid model that incorporates the autoregressive fractionally integrated moving average (ARFIMA), empirical wavelet (EW) transform, and local linear wavelet neural network (LLWNN) approaches to produce an ARFIMA-EWLLWNN model. Our methodologies integrate the advantages of the long-memory model, EW decomposition technique, artificial neural network structure, and backpropagation and particle swarm optimization learning algorithms. The experimental results of the optimized hybrid approach outperform some classic models by providing more accurate out-of-sample forecasts over longer horizons. The model proves to be the most appropriate Bitcoin forecasting technique. Moreover, the implemented method produces smaller prediction errors than other computing techniques.

A Bayesian network structure learning method for optimizing ordering search operator

Local search algorithm in ordering space is a good method which can effectively improve the efficiency of bayesian network structure learning. However, the existing algorithms usually have problems such as insufficient order optimization, low learning accuracy, and easy stop at a local optimal. In order to solve these problems, the local search algorithm in ordering space is studied, and a new method to improve the accuracy of bayesian network structure learning by optimizing order search operator is proposed. Combining the iterative local search algorithm with the window operator to search the neighborhood of a given order in the ordering space, the probability of the algorithm falling into the local optimal value is reduced, and the network structure with higher quality is obtained. Experimental results show that comparing with the bayesian network structure learning algorithm in network structure space, the learning efficiency of the present algorithm is improved by 54.12%. Comparing with the bayesian network structure learning algorithm in ordering space, the learning accuracy of the present algorithm is improved by 2.33%.

Score-based Bayesian network structure learning algorithms for modeling radioisotope levels in nuclear power plant reactors

Radioactive corrosion products released into the primary coolant loop dominate the final shutdown radiation fields of pressurized water reactors. Thus, reducing the concentration of these corrosion products is a paramount duty in the optimization process of the reactor performance. However, the complexity and uncertainty present in this process make it difficult to predict their evolution in a theoretical way. We propose the application of structural learning of Bayesian networks to discover the complex relations between the corrosion products and the most relevant variables in the primary loop, giving rise to probabilistic models that obtain accurate and reliable predictions of the corrosion products. Our analysis of 5 power plants demonstrates that our approach results in simpler and more reliable models. Additionally, we conclude that the learned structures may represent an interpretable tool for power plant technicians since they reveal useful information that can be directly employed to improve the reactor operation.

Data poisoning attacks detection based on the convergence of the Peter and Clark algorithm and Bayesian adversarial federated learning

Effective Bayesian network structure learning algorithms, such as the Peter and Clark (PC) algorithm, must be designed and used with data integrity as a top priority. Deep learning models can be jointly built by thousands of participants; thanks to the innovative distributed learning technology known as federated learning. This research proposes a technique for the detection of data poisoning attacks along with enhancing secure data transmission and routing by utilizing the integration of the PC algorithm and federated learning. Data integrity of the network is likewise enhanced using the Convergence of the PC algorithm. Detection of attacks in established secure data transmission is carried out using Bayesian adversarial federated learning. We provided an optimization-based model poisoning approach and introduced adversarial neurons into the redundant area of a neural network by assessing model capacity. It should be highlighted that while those redundant neurons have little relevance to the primary goal of federated learning, they are crucial for poisoning attacks. Numerical tests show that the suggested approach can get beyond defense mechanisms and have a high attack success rate.

An efficient Bayesian network structure learning algorithm based on structural information

An efficient Bayesian network structure learning algorithm based on structural information

Bayesian Network Structure Learning Algorithm Combining Improved Dragonfly Optimization

Bayesian Network Structure Learning Algorithm Combining Improved Dragonfly Optimization

Decomposition-Based Bayesian Network Structure Learning Algorithm for Abnormity Diagnosis Model for Coal Mill Process

In the structure learning of the large-scale Bayesian network (BN) model for the coal mill process, taking the view of the problem that the decomposition-based method cannot guarantee the sufficient learning of abnormal state node neighborhood in the diagnosis model, this paper proposes a new BN structure learning method based on decomposition. Firstly, a sketch is constructed based on an improved Markov blanket discovery algorithm and edge thickening and thinning. Second, the node centrality of k-path is used to search the important nodes, and the subgraph decomposition is realized by extracting these important nodes and their neighborhoods from the sketch. Then, through the targeted design of subgraph de-duplication, subgraph learning, and subgraph reorganization methods, the learning of large-scale BN is realized. This method is applied to public data sets, and its advantages and disadvantages are analyzed by comparing them with other methods. The advantage of the BN structure learning method of the abnormal condition diagnosis model is further verified by applying the method to the coal mill process, which is consistent with the original design intention.

A Dynamic Programming Bayesian Network Structure Learning Algorithm Based on Mutual Information

Existing Bayesian network (BN) structure learning algorithms based on dynamic programming have high computational complexity and are difficult to apply to large-scale networks. Therefore, this paper proposes a Dynamic Programming BN structure learning algorithm based on Mutual Information, the MIDP (Dynamic Programming Based on Mutual Information) algorithm. The algorithm uses mutual information to build the maximum spanning tree and [Formula: see text]-order matrix, and introduces a penalty coefficient d based on the matrix-based node removal strategy, so as to reduce the number of scoring calculations and time consumption of the algorithm. Simulation results show that, compared with DP, SMDP and MEDP algorithms, the MIDP algorithm can reduce the calculation times and time consumption of algorithm scores while maintaining the accuracy of the algorithm when selecting the appropriate [Formula: see text] value.

Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.

Greedy structure learning from data that contain systematic missing values

Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume missing data are missing at random, such as the Expectation-Maximisation algorithm. Because missing data are often systematic, there is a need for more pragmatic methods that can effectively deal with data sets containing missing values not missing at random. The absence of approaches that deal with systematic missing data impedes the application of BN structure learning methods to real-world problems where missingness are not random. This paper describes three variants of greedy search structure learning that utilise pairwise deletion and inverse probability weighting to maximally leverage the observed data and to limit potential bias caused by missing values. The first two of the variants can be viewed as sub-versions of the third and best performing variant, but are important in their own in illustrating the successive improvements in learning accuracy. The empirical investigations show that the proposed approach outperforms the commonly used and state-of-the-art Structural EM algorithm, both in terms of learning accuracy and efficiency, as well as both when data are missing at random and not at random.

A Bayesian Network Structure Learning Algorithm Based on Probabilistic Incremental Analysis and Constraint

To address the problem of low efficiency of the existing hill-climbing algorithm in Bayesian network structure learning, this paper proposes a Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints. The algorithm constructs a suitable measure for representing the degree of node association in Bayesian networks based on the principle of random forest feature extraction; then uses the method to construct the initial Bayesian network structure and constrains the search space by setting a corresponding threshold for the probability increment centered on each node; finally takes the initial Bayesian network as the starting point and learns it by the forbidden hill-climbing search and BIC scoring method to obtain the optimal Bayesian network structure. Experimental results show that the correlation degree measure and mutual information proposed in this paper have an approximate correlation expression effect; compared with other Bayesian network structure learning algorithms of the same type, the method in this paper has a faster operation speed while ensuring the quality of the learned network. The experimental results show that the Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints is an effective and efficient Bayesian network structure learning algorithm.

Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning

Network structure learning algorithms have aided network discovery in fields such as bioinformatics, neuroscience, ecology and social science. However, challenges remain in learning informative networks for related sets of tasks because the search space of Bayesian network structures is characterized by large basins of approximately equivalent solutions. Multitask algorithms select a set of networks that are near each other in the search space, rather than a score-equivalent set of networks chosen from independent regions of the space. This selection preference allows a domain expert to see only differences supported by the data. However, the usefulness of these algorithms for scientific datasets is limited because existing algorithms naively assume that all pairs of tasks are equally related. We introduce a framework that relaxes this assumption by incorporating domain knowledge about task-relatedness into the learning objective. Using our framework, we introduce the first multitask Bayesian network algorithm that leverages domain knowledge about the relatedness of tasks. We use our algorithm to explore the effect of task-relatedness on network discovery and show that our algorithm learns networks that are closer to ground truth than naive algorithms and that our algorithm discovers patterns that are interesting.

Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is ‘best’. This is partly because there is no agreed evaluation approach to determine their effectiveness. Moreover, each algorithm is based on a set of assumptions, such as complete data and causal sufficiency, and tend to be evaluated with data that conforms to these assumptions, however unrealistic these assumptions may be in the real world. As a result, it is widely accepted that synthetic performance overestimates real performance, although to what degree this may happen remains unknown. This paper investigates the performance of 15 state-of-the-art, well-established, or recent promising structure learning algorithms. We propose a methodology that applies the algorithms to data that incorporates synthetic noise, in an effort to better understand the performance of structure learning algorithms when applied to real data. Each algorithm is tested over multiple case studies, sample sizes, types of noise, and assessed with multiple evaluation criteria. This work involved learning approximately 10,000 graphs with a total structure learning runtime of seven months. In investigating the impact of data noise, we provide the first large scale empirical comparison of BN structure learning algorithms under different assumptions of data noise. The results suggest that traditional synthetic performance may overestimate real-world performance by anywhere between 10% and more than 50%. They also show that while score-based learning is generally superior to constraint-based learning, a higher fitting score does not necessarily imply a more accurate causal graph. The comparisons extend to other outcomes of interest, such as runtime, reliability, and resilience to noise, assessed over both small and large networks, and with both limited and big data. To facilitate comparisons with future studies, we have made all data, raw results, graphs and BN models freely available online.

An Improved Particle Swarm Optimization Algorithm for Bayesian Network Structure Learning via Local Information Constraint

At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm’s optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.