Problem
Research questions and friction points this paper is trying to address.
Bayesian Network Fusion
Treewidth Constraints
Dependency Preservation
Genetic Algorithms
Overfitting
Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning
🤖 AI Summary
本文针对贝叶斯网络融合中的复杂性和依赖性保留问题，提出了一种基于遗传算法的共识框架，并通过预融合边修剪控制树宽，实验表明该方法优于现有方法。
📝 Abstract
Bayesian Network (BN) fusion combines multiple input networks into a single structure, balancing dependency preservation with computational tractability. While unrestricted fusion retains all dependencies, it often results in overly complex networks with high treewidth, which affects inference scalability. Limited fusion mitigates this by pruning edges to control treewidth but risks overfitting to input-specific noise and omitting dependencies from the original BNs. This paper introduces a consensus framework that prioritizes shared structures among input networks while enforcing treewidth constraints, ensuring a good consensus. We propose genetic algorithms with advanced initialization, specialized operators, and a tailored fitness function. Additionally, we adapt existing methods to this problem and implement greedy baselines for benchmarking and further optimization. Experiments on synthetic and real-world BNs show the superiority of the proposed genetic algorithms over the adapted methods and greedy baselines.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Genetic Algorithms
Pre-Fusion Edge Pruning
Consensus Framework
Bayesian Network Fusion
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