A New BN Structure Learning Mechanism Based on Decomposability of Scoring Functions

Li, Guoliang; Xing, Lining; Chen, Yingwu

doi:10.1007/978-3-662-49014-3_19

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The experiments show advantages in the quality of the fitness function in a comparison between a Particle Swarm Optimization algorithm (PSO) and a GA. In [40], a hybrid algorithm between the maximal information coefficient and binary PSO was proposed. The experimental results show that without a given node ordering, this algorithm has better performance than the other five of the state of the art algorithms.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

Aguilera-Rueda

Cruz-Ramírez

Mezura-Montes

2020

MCA

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We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is based on the well-known NSGA-II algorithm. The core idea is to reduce the implicit selection bias-variance decomposition while identifying a set of competitive models using both objectives. Numerical results suggest that, in stark contrast to the single-objective approach, our bi-objective approach is useful to find competitive Bayesian networks especially in the complexity. Furthermore, our approach presents the end user with a set of solutions by showing different Bayesian network and their respective MDL and classification accuracy results.

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Section: Related Workmentioning

confidence: 99%

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

Aguilera-Rueda

Cruz-Ramírez

Mezura-Montes

2020

MCA

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“…Li et al [20] proposed a PSO algorithm based on the maximum amount of information. This algorithm uses the chaotic mapping method to represent the BN structure.…”

Section: Introductionmentioning

confidence: 99%

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

Liu,

Li,

Zhang

et al. 2023

Computers, Materials &Amp; Continua

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To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial particles. In the second stage, the update mechanism is redefined, so that each step of the update process is consistent with the current structure which forms a one-to-one correspondence. At the same time, the "self-awakened" mechanism is added to prevent precocious particles from being part of the best. After the fitness value of the particle converges prematurely, the activation operation makes the particles jump out of the local optimal values to prevent the algorithm from converging too quickly into the local optimum. Finally, the standard network dataset was compared with other algorithms. The experimental results showed that the algorithm could find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm's effectiveness.

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A New Bayesian Network Structure Learning Algorithm Mechanism Based on the Decomposability of Scoring Functions

Xing

Zhang

et al. 2017

IEICE Trans. Fundamentals

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A New BN Structure Learning Mechanism Based on Decomposability of Scoring Functions

Cited by 3 publications

References 15 publications

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

A New Bayesian Network Structure Learning Algorithm Mechanism Based on the Decomposability of Scoring Functions

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