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Creating an incident investigation framework for a complex socio-technical system: Application of multi-label text classification and bayesian network structure learning

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 260卷

作者： Dehkordi, Mohammadreza Karimi Sattari, Fereshteh Lefsrud, Lianne Univ Alberta Sch Engn Safety & Risk Management Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

The power distribution sector presents a complex socio-technical system where accidents frequently occur from various technical, human, environmental, and organizational factors, resulting in fatalities and substantial economic losses. The dynamic operational environment and complex interactions among the causal factors further complicate effective risk management and accident prevention. This research proposes a methodology to identify various risk factors and develop causal networks representing the complex relationships among these factors in power distribution incident reports. First, machine learning multi-label text classification identifies the risk factors from the incident reports. Then, the relationship among these factors is determined by integrating experts' domain knowledge and data-driven bayesian network structure learning approaches. Finally, the most influential causal factors and their direct/indirect effects on the incidents are identified, and proper risk control measures are recommended. The proposed methodology is applied to an incident database from a Canadian power distribution company, covering power outages, injuries, environmental issues, and near misses collected from 2013 to 2020. The results highlight that human and technical factors are the most influential and affected by organizational and environmental factors. Considering their complex interaction, implementing targeted risk management for high-risk direct/indirect causal factors could prevent further incidents and improve the companies' overall safety.

关键词： Incident investigation Socio-technical system Multi-label text classification Causal analysis bayesian network structure learning Human and organizational factors Power distribution

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A Hybrid Method:Resolving the Impact of Variable Ordering in bayesian network structure learning

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Fudan Journal of the Humanities and Social Sciences 2025年第1期18卷 175-191页

作者： Minglan Li Yueqin Hu Faculty of Psychology Beijing Normal University19 Xinjiekouwai St.Beijing100875China

In recent years,the development of machine learning has introduced new analytical methods to theoretical research,one of which is bayesian network—a probabilistic graphical model well-suited for modelling complex non-deterministic systems.A recent study has revealed that the order in which variables are read from data can impact the structure of a bayesian network(Kitson and Constantinou in The impact of variable ordering on bayesian network structure learning,*** preprint arXiv:2206.08952).However,in empirical studies,the variable order in a dataset is often arbitrary,leading to unreliable *** address this issue,this study proposed a hybrid method that combined theory-driven and data-driven approaches to mitigate the impact of variable ordering on the learning of bayesian network *** proposed method was illustrated using an empirical study predicting depression and aggressive behavior in high school *** results demonstrated that the obtained bayesian network structure is robust to variable orders and theoretically *** commonalities and specificities in the network structure of depression and aggressive behavior are both in line with theorical expectations,providing empirical evidence for the validity of the hybrid method.

关键词： bayesian network structure learning Complete partially directed acyclic graph Depression Aggressive behavior

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FedGES: A Federated learning Approach for bayesian network structure learning 27th

FedGES: A Federated Learning Approach for Bayesian Network S...

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27th International Conference on Discovery Science

作者： Torrijos, Pablo Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Inst Invest Informat Albacete I3A Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informaticos Albacete 02071 Spain

ISBN: (纸本)9783031789793;9783031789809

bayesian network (BN) structure learning traditionally centralizes data, raising privacy concerns when data is distributed across multiple entities. This research introduces Federated GES (FedGES), a novel Federated learning approach tailored for BN structure learning in decentralized settings using the Greedy Equivalence Search (GES) algorithm. FedGES uniquely addresses privacy and security challenges by exchanging only evolving network structures, not parameters or data. It performs collaborative model development, using structural fusion to combine the limited models generated by each client in successive iterations. A controlled structural fusion is also proposed to enhance client consensus when adding any edge. Experimental results on various BNs from bnlearn's BN Repository validate the effectiveness of FedGES, particularly in high-dimensional (a large number of variables) and sparse data scenarios, offering a practical and privacy-preserving solution for real-world BN structure learning.

关键词： Federated learning bayesian network structure learning bayesian network fusion/aggregation

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bayesian network structure learning using quantum generative models

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QUANTUM MACHINE INTELLIGENCE 2024年第2期6卷 1-14页

作者： Ohno, Hiroshi Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi Japan

bayesian network structure learning (BNSL) is a popular NP-hard optimization problem in the classical machine learning community. Given data, the network structure is optimized under the constraints of a directed acyclic graph and network scores using a cost function representing the constraints. In this study, we present BNSL using quantum generative models (QGMs) as a novel quantum machine learning application. QGMs are based on a quantum circuit composed of Pauli Y-rotation gates and controlled Pauli X or Z gates for quantum entanglement. Two real datasets are used to verify the comparative performance compared to classical counterpart GMs based on a three-layer neural network. For the training stage of the models, a hybrid quantum-classical framework is used. Due to the constraint-based cost function, classical data encoding is unnecessary, and the QGMs are trained so as to realize the desired output probability in one measurement. Simulation results show that QGMs achieve a comparative or better performance. In addition, we find a significant speed-up of the QGM compared to classical counterpart GMs. We believe that a combination of constraint-based cost functions and QGMs is useful to achieve such speed-ups.

关键词： Quantum generative models Quantum machine learning Hybrid quantum-classical machine learning bayesian network structure learning Neural networks

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bayesian network structure learning from Big Data: A Reservoir Sampling Based Ensemble Method 1

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International Workshop on Database Systems for Advanced Applications (DASFAA)

作者： Tang, Yan Xu, Zhuoming Zhuang, Yuanhang Hohai Univ Coll Comp & Informat Nanjing 210098 Jiangsu Peoples R China

ISBN: (数字)9783319320557

ISBN: (纸本)9783319320557;9783319320540

bayesian network (BN) learning from big datasets is potentially more valuable than learning from conventional small datasets as big data contain more comprehensive probability distributions and richer causal relationships. However, learning BNs from big datasets requires high computational cost and easily ends in failure, especially when the learning task is performed on a conventional computation platform. This paper addresses the issue of BN structure learning from a big dataset on a conventional computation platform, and proposes a reservoir sampling based ensemble method (RSEM). In RSEM, a greedy algorithm is used to determine an appropriate size of sub datasets to be extracted from the big dataset. A fast reservoir sampling method is then adopted to efficiently extract sub datasets in one pass. Lastly, a weighted adjacent matrix based ensemble method is employed to produce the final BN structure. Experimental results on both synthetic and real-world big datasets show that RSEM can perform BN structure learning in an accurate and efficient way.

关键词： bayesian network structure learning Reservoir sampling Ensemble method Probabilistic approximation Big data

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Quantum approximate optimization algorithm for bayesian network structure learning

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QUANTUM INFORMATION PROCESSING 2022年第1期22卷 1-28页

作者： Soloviev, Vicente P. Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Computat Intelligence Grp Campus Montegancedo Madrid Spain

bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide range of advantages that can be exploited to solve optimization tasks that cannot be addressed in an efficient way when utilizing classic computing approaches. In this work, a specific type of variational quantum algorithm, the quantum approximate optimization algorithm, was used to solve the bayesian network structure learning problem, by employing 3n(n - 1)/2 qubits, where n is the number of nodes in the bayesian network to be learned. Our results showed that the quantum approximate optimization algorithm approach offers competitive results with stateof-the-art methods and quantitative resilience to quantum noise. The approach was applied to a cancer benchmark problem, and the results justified the use of variational quantum algorithms for solving the bayesian network structure learning problem.

关键词： Quantum approximate optimization algorithm Variational quantum algorithm Quantum optimization bayesian network structure learning

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A bayesian network structure learning Algorithm Based on Probabilistic Incremental Analysis and Constraint

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IEEE ACCESS 2022年 10卷 130719-130732页

作者： Liu, Haoran Cui, Shaopeng Li, Sheng Wang, Niantai Shi, Qianrui Cai, Yanbin Zhang, Liyue Liu, Dayan Yanshan Univ Informat Sci & Engn Dept Qinhuangdao 066004 Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

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.

关键词： Probability increment bayesian network structure learning constrained search space Hill-climbing algorithm

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An efficient bayesian network structure learning algorithm using the strategy of two-stage searches

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INTELLIGENT DATA ANALYSIS 2020年第5期24卷 1087-1106页

作者： Guo, Huiping Li, Hongru Northeastern Univ Informat Sci & Engn POB 13511 St 3Wenhua Rd Shenyang 110819 Liaoning Peoples R China

It is important for bayesian network (BN) structure learning, a NP-problem, to improve the accuracy and hybrid algorithms are a kind of effective structure learning algorithms at present. Most hybrid algorithms adopt the strategy of one heuristic search and can be divided into two groups: one heuristic search based on initial BN skeleton and one heuristic search based on initial solutions. The former often fails to guarantee globality of the optimal structure and the latter fails to get the optimal solution because of large search space. In this paper, an efficient hybrid algorithm is proposed with the strategy of two-stage searches. For first-stage search, it firstly determines the local search space based on Maximal Information Coefficient by introducing penalty factors p(1), p(2), then searches the local space by Binary Particle Swarm Optimization. For second-stage search, an efficient ADR (the abbreviation of Add, Delete, Reverse) algorithm based on three basic operators is designed to extend the local space to the whole space. Experiment results show that the proposed algorithm can obtain better performance of BN structure learning.

关键词： bayesian network structure learning hybrid algorithms penalty factors binary particle swarm optimization algorithm ADR algorithm

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An Efficient bayesian network structure learning Strategy

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NEW GENERATION COMPUTING 2017年第1期35卷 105-124页

作者： Suzuki, Joe Osaka Univ Dept Math 1-1 Machikaneyamacho Toyonaka Osaka 5600043 Japan

This paper addresses the problem of efficiently finding an optimal bayesian network structure for maximizing the posterior probability. In particular, we focus on the B& B strategy to save the computational effort associated with finding the largest score. To make the search more efficient, we need a tighter upper bound so that the current score can exceed it more easily. We find two upper bounds and prove that they are tighter than the existing one (Campos and Ji, J Mach Learn Res 12(3):663-689, 2011). Finally, we demonstrate that the proposed two bounds render the search to be much more efficient using the Alarm and Insurance data sets. For example, the search is twice to three times faster for and almost twice faster for . We also experimentally verify that the overhead due to replacing the existing pruning rule by the proposed one is negligible.

关键词： Parent set Branch and bound (B& B) Upper bound Score Maximizing the posterior probability bayesian network structure learning

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Comparative Analysis of Search and Score Metaheuristics for bayesian network structure learning Using Node Juxtaposition Distributions

Comparative Analysis of Search and Score Metaheuristics for ...

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11th International Conference on Parallel Problem Solving from Nature

作者： Wu, Yanghui McCall, John Corne, David Robert Gordon Univ IDEAS Res Inst Aberdeen AB9 1FR Scotland Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9783642158438

learning bayesian networks from data is an NP-hard problem with important practical applications. Metaheuristic search on the space of node orderings combined with deterministic construction and scoring of a network is a well-established approach. The comparative performance of different search and score algorithms is highly problem-dependent and so it is of interest to analyze, for benchmark problems with known structures, the relationship between problem features and algorithm performance. In this paper, we investigate four combinations of search (Genetic Algorithms or Ant Colony Optimization) with scoring (K2 or Chain). We relate node juxtaposition distributions over a number of runs to the known problem structure, the algorithm performance and the detailed algorithmic processes. We observe that;for different reasons, AGO and Chain both focus the search on a narrower range of orderings. This works well when the underlying structure is compatible but poorly otherwise. We conclude by suggesting future directions for research.

关键词： Ant Colony Optimization Genetic Algorithm bayesian network structure learning Node Ordering Chain Model K2

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