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IEEE Congress on Evolutionary Computation (CEC)

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)9781424478354

Algorithms for learning the structure of bayesian networks (BN) from data are the focus of intense research interest. Search-and-score algorithms using nature-inspired metaheuristics are an important strand of this research;however performance is variable and strongly problem-dependent. In this paper we use fitness landscape analysis to explain empirically-observed performance differences between particular search-and-score algorithms on two well-studied benchmark problems. We investigate the average landscape discovered by random walks around optimal points in the space of BN node orderings. Differences in algorithm performance are explained in terms of these landscapes, which in turn are related to properties of the BN structures. These initial findings suggest that fitness landscape analysis is a promising approach for explaining existing empirical performance comparisons with further potential for understanding the relative difficulty of benchmark problems and the robustness of particular algorithms.

关键词： bayesian network structure learning search-and-score algorithms fitness landscape analysis topological sort data modelling

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Adaptive bayesian network structure learning from Big Datasets 4th

Adaptive Bayesian Network Structure Learning from Big Datase...

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22nd International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Tang, Yan Zhang, Qidong Liu, Huaxin Wang, Wangsong Hohai Univ Coll Comp & Informat Nanjing 210098 Jiangsu Peoples R China

ISBN: (纸本)9783319557052;9783319557045

Since big data contain more comprehensive probability distributions and richer causal relationships than conventional small datasets, discovering bayesian network (BN) structure from big datasets is becoming more and more valuable for modeling and reasoning under uncertainties in many areas. Facing big data, most of the current BN structure learning algorithms have limitations. First, learning BNs structure from big datasets is an expensive process that requires high computational cost, often ending in failure. Second, given any dataset as input, it is very difficult to choose one algorithm from numerous candidates for consistently achieving good learning accuracy. To address these issues, we introduce a novel approach called Adaptive bayesian network learning (ABNL). ABNL begins with an adaptive sampling process that extracts a sufficiently large data partition from any big dataset for fast structure learning. Then, ABNL feeds the data partition to different learning algorithms to obtain a collection of BN structures. Lastly, ABNL adaptively chooses the structures and merge them into a final network structure using an ensemble method. Experimental results on four big datasets show that ABNL leads to a significantly improved performance than whole dataset learning and more accurate results than baseline algorithms.

关键词： bayesian network structure learning bayesian score Big data sampling Ensemble method

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Landscape Analysis for Hyperheuristic bayesian network structure learning on Unseen Problems

Landscape Analysis for Hyperheuristic Bayesian Network Struc...

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IEEE Congress on Evolutionary Computation (CEC)

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

ISBN: (纸本)9781467315098

bayesian network (BN) structure learning is an NP hard problem. Search and score algorithms are one of the main approaches proposed for learning BN structure from data. Previous research has shown that the relative performances of such algorithms are problem dependent and that fitness landscape analysis can be used to characterize the difficulty of the search for different scoring functions. In this paper, we construct a classifier based on fitness landscape analysis and receiver operating characteristic curves. The classifier labels search landscapes with the most suitable scoring function. We train the classifier on a number of standard benchmark functions. The classifier forms the basis for a selective hyperheuristic algorithm. This uses an initial landscape analysis stage to select a scoring function using the classifier. The hyperheuristic algorithm is tested on a distribution of unseen problems based on mutations of the standard benchmarks. Our results establish that the hyperheuristic performs better than a uniformly random scoring function selection approach that omit the landscape analysis stage. Therefore the effects on performance of problem-dependency can be significantly reduced.

关键词： bayesian network structure learning search-and-score algorithms fitness landscape analysis Receiver Operating Characteristic classifier hyperheuristic

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The Role of Crossover Operator in bayesian network structure learning Performance: a Comprehensive Comparative Study and New Insights 17

The Role of Crossover Operator in Bayesian Network Structure...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Contaldi, Carlo Vafaee, Fatemeh Nelson, Peter C. Univ Illinois 1200 West Harrison St Chicago IL 60607 USA Univ Sydney Sydney NSW 2006 Australia

ISBN: (纸本)9781450349208

bayesian network (BN) structure learning is a complex search problem, generally characterized by multimodality and epistasis. Genetic Algorithms (GAs) have been extensively used to pursue the BN structure learning task. This paper presents a new approach which incorporates the structural properties of the problem into GA mechanisms. The proposed approach uses a new recombination operator named Parent Set crossover, capable of reducing the disruptive action of the recombination process and enhancing its exploitative power. The new operator has been compared with a comprehensive set of other crossover operators as part of two genetic strategies: a canonical GA and a GA with an adaptive mutation scheme. All examined crossover operators were applied on both canonical and adaptive GAs and then compared in terms of various performance metrics. The experiments involve performance measures at the end of evolution as well as their convergence behavior across generations. The performance of the proposed method was also compared with the state-of-the-art non-evolutionary BN structure learning algorithms. Results show that the proposed recombination method enhances the algorithmic efficiency over a variety of test cases of different size.

关键词： bayesian network structure learning genetic algorithms crossover exploitation

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Two Novel Ant Colony Optimization Approaches for bayesian network structure learning

Two Novel Ant Colony Optimization Approaches for Bayesian Ne...

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2010 IEEE World Congress on Computational Intelligence

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)9781424481262

learning bayesian networks from data is an NP-hard problem with important practical applications. Several researchers have designed algorithms to overcome the computational complexity of this task. Difficult challenges remain however in reducing computation time for structure learning in networks of medium to large size and in understanding problem-dependent aspects of performance. In this paper, we present two novel algorithms (ChainACO and K2ACO) that use Ant Colony Optimization (ACO). Both algorithms search through the space of orderings of data variables. The ChainACO approach uses chain structures to reduce computational complexity of evaluation but at the expense of ignoring the richer structure that is explored in the K2ACO approach. The novel algorithms presented here are ACO versions of previously published GA approaches. We are therefore able to compare ACO vs GA algorithms and Chain vs K2 evaluations. We present a series of experiments on three well-known benchmark problems. Our results show problem-specific trade-offs between solution quality and computational effort. However it seems that the ACO-based approaches might be favored for larger problems, achieving better fitnesses and success rate than their GA counterparts on the largest network studied in our experiments.

关键词： ACO bayesian network structure learning NP-hard problem ant colony optimization approaches belief networks computational complexity computational complexity data variables optimisation

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Transfer learning-based Hybrid Approach for bayesian network structure learning

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第7期31卷 2260003-2260003页

作者： Jose, Sonu Louis, Sushil Dascalu, Sergiu Liu, Siming Univ Nevada Dept Comp Sci & Engn 1664 N Virginia St Reno NV 89557 USA Missouri State Univ Dept Comp Sci & Engn 901 S Natl Ave Springfield MO 65897 USA

bayesian network is a graphical model that is widely used to perform probabilistic reasoning. However, learning the structure of bayesian network is a complex task. In this paper, we propose a hybrid structure learning algorithm that has two phases: a constraint-based phase to reduce the search space and a score-and-search phase that employs case-injected genetic algorithms for determining the optimal structure from the reduced space of structures. We use a case-injected genetic algorithm-based hybrid approach for the structure learning in order to improve the learning accuracy over similar problems. A case-injected genetic algorithm is the augmentation of a case-based memory with the Genetic Algorithm (GA). Thereby, it finds near-optimal solutions in fewer generations compared to GA. Our method stores relevant or partial solutions in a case-base while solving the problems and utilizes those stored solutions on new similar problems. We use small-to-very large networks for assessing our viability of our approach. In this paper, a series of experiments are conducted on datasets generated from four benchmark bayesian networks. We compare our method against GA-based hybrid approach and a state-of-the-art algorithm, Max-Min Hill Climbing (MMHC). Presented results indicate an enhanced improvement of our approach over GA and MMHC in learning the bayesian network structures.

关键词： bayesian network structure learning graphical models case-injected genetic algorithms genetic algorithms transfer learning

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GOBNILP: learning bayesian network structure with integer programming 10

GOBNILP: Learning Bayesian network structure with integer pr...

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10th International Conference on Probabilistic Graphical Models (PGM)

作者： Cussens, James Univ Bristol Dept Comp Sci Bristol Avon England

The GOBNILP system for learning bayesian networks is presented. Both the Python and C implementations are discussed. The usefulness of learning multiple BNs is highlighted. Current work on 'pricing in' new int... 详细信息

关键词： bayesian network structure learning integer programming C SCIP Python

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learning bayesian network structure from Large-scale Datasets 4

Learning Bayesian Network Structure from Large-scale Dataset...

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4th International Conference on Advanced Cloud and Big Data (CBD)

作者： Hong, Yu Xia, Xiaoling Le, Jiajin Zhou, Xiangdong Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9781509036776

bayesian network is one of the most classical and effective models in big data graph algorithms. Aiming at the problem of learning bayesian network structure from large-scale datasets, a novel algorithm with the combination of Information theory, Tabu search and Akaike Information Criterion (AIC) called ITA is proposed. Firstly, a dimension reduction algorithm based on information theory is used to filter non-target variables. The variables closely related to the target are picked as the vertexes in bayesian network. Then choosing AIC as the scoring method and Tabu Search as the heuristic algorithm, a new learning algorithm is adopted to build the global optimal structure. Experimental results demonstrate that ITA algorithm can obtain core causal relationships from largescale datasets in certain area accurately and construct clean and straightforward bayesian network structure at a lower time cost. Therefore, ITA is an effective and efficient big data graph algorithm for learning bayesian network structure from largescale datasets.

关键词： Keywords big data graph algorithm bayesian network structure learning large-scale datasets information theory Tabu Search AIC

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Large-Scale Hierarchical Causal Discovery via Weak Prior Knowledge

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第5期37卷 2695-2711页

作者： Wang, Xiangyu Ban, Taiyu Chen, Lyuzhou Lyu, Derui Zhu, Qinrui Chen, Huanhuan Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230052 Peoples R China

Causal discovery faces significant challenges as the number of hypotheses grows exponentially with the number of variables. This complexity becomes particularly daunting when dealing with large sets of variables. We introduce a novel divide-and-conquer method that uniquely handles this challenge. The existing division strategies often rely on conditional independency (CI) tests or data-driven clustering to split variables, which can suffer from the typical data scarcity in large-scale settings, thus leading to inaccurate division results. The proposed method overcomes this by implementing a data-independent division strategy, which constructs a prior structure, informed by potential causal relationships identified using a Large Language Model (LLM), to guide recursively dividing variables into sub-sets. This approach avoids the impact of data insufficiency and is robust against potential incompleteness in the prior structure. In the merging phase, we adopt a score-based refinement strategy to address fake causal links caused by hidden variables in sub-sets, which eliminates edges in the intersected parts of sub-sets to optimize the score of local structures. While maintaining both correctness and completeness under the faithfulness assumption, this novel merging approach demonstrates enhanced performance than the conventional CI-test based merging strategy in practical scenarios. Empirical evaluations on various large-scale datasets demonstrate the proposed approach's superior accuracy and efficiency compared to existing causal discovery methods.

关键词： Cause effect analysis Merging Cognition Accuracy Reviews Faces Complexity theory Bayes methods Training Stability criteria Causal structure learning large language model bayesian network structure learning

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Governance and sustainability of distributed continuum systems: a big data approach

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JOURNAL OF BIG DATA 2023年第1期10卷 53页

作者： Donta, Praveen Kumar Sedlak, Boris Pujol, Victor Casamayor Dustdar, Schahram TU Wien Distributed Syst Grp A-1040 Vienna Austria

Distributed computing continuum systems (DCCS) make use of a vast number of computing devices to process data generated by edge devices such as the Internet of Things and sensor nodes. Besides performing computations, these devices also produce data including, for example, event logs, configuration files, network management information. When these data are analyzed, we can learn more about the devices, such as their capabilities, processing efficiency, resource usage, and failure prediction. However, these data are available in different forms and have different attributes due to the highly heterogeneous nature of DCCS. The diversity of data poses various challenges which we discuss by relating them to big data, so that we can utilize the advantages of big data analytical tools. We enumerate several existing tools that can perform the monitoring task and also summarize their characteristics. Further, we provide a general governance and sustainable architecture for DCCS, which reflects the human body's self-healing model. The proposed model has three stages: first, it analyzes system data to acquire knowledge;second, it can leverage the knowledge to monitor and predict future conditions;and third, it takes further actions to autonomously solve any issue or to alert administrators. Thus, the DCCS model is designed to minimize the system's downtime while optimizing resource usage. A small set of data is used to illustrate the monitoring and prediction of the performance of a system through bayesian network structure learning. Finally, we discuss the limitations of the governance and sustainability model, and we provide possible solutions to overcome them and make the system more efficient.

关键词： Distributed computing continuum systems Big data analytics Governance and sustainability Self-healing Representation learning bayesian network structure learning

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