MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application

He, Chuchao; Gao, Xiaoguang; Wan, Kaifang

doi:10.1049/cje.2019.11.004

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Mutual information of features is calculated by equation (12). Assign the maximum mutual information value to equation (13). Determine whether the feature edges are connected or not, and determine the initial network structure.…”

Section: Conditional Independence Test (Ci Test)mentioning

confidence: 99%

“…Still, for large BN networks, expert knowledge is difficult to obtain, and the uncertainty of its constraint content as a limitation impacts the goodness of structure learning. HE Chuchao and GAO Xiaoguang [13] added the prior as a constraint in the OS+ algorithm and used the maximumminimum parent-child algorithm (MMPC) to obtain the set of parents and children (PC) of each node, and then built the BN model. This method can improve modeling efficiency, but it takes too long and falls to the local optimum.…”

mentioning

confidence: 99%

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A Bayesian network optimization method for ranked search under constraints

WANG

XUN

Cao

2022

Preprint

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Bayesian networks can deal with uncertainty and polymorphism and are widely used in the field of fault diagnosis. However, the directed acyclic graph (DAG) space is large, and it is an NP-hard problem to get the optimal BN structure from the data. The current research combines constraint-based and search-based methods to search for the optimal BN structure in the DAG to solve this problem. However, when the constraint space is constant and imprecise, the optimal solution is most likely to be lost, resulting in low model accuracy. In addition, the search space expands with the increase of the number of nodes, which leads to the structure quickly falling into local optimum and low learning efficiency. Therefore, this paper proposes a Bayesian Network Modeling for Ranking Search under Constrained Conditions (BNMRSCC) method to find a BN structure that fits the data set in the constrained search space. Firstly, in the constraint phase, the MIC algorithm and CI test are used as double constraints to limit the search space and two estimatorsB anda are introduced in the original MIC algorithm to change the search space during the operation to reduce the computation time and improve the learning efficiency. Secondly, in the search phase, the XGboost algorithm is used to calculate the feature importance scores and rank them, and the search space is further determined based on the ranking under the double constraint. Finally, the feature ranking is used as the input of the K2 algorithm to improve the accuracy of network inference. The experimental results show that the model built by this algorithm is better and more effective for learning BN structures.

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Section: Conditional Independence Test (Ci Test)mentioning

confidence: 99%

mentioning

confidence: 99%

A Bayesian network optimization method for ranked search under constraints

WANG

XUN

Cao

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In general, they are obtained by direct learning or defined by expert experience. [25][26][27][28][29] Typical direct learning methods refer to the K2 algorithm, 26 search optimization algorithms, 27 and dependency analysis. 28 However, actual civil structures are often statically indeterminate with high redundancy, resulting in a large number of BN nodes and the complex causality among BN nodes.…”

Section: Introductionmentioning

confidence: 99%