通过预融合边剪枝实现易处理贝叶斯网络融合的遗传算法
Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning
- Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对贝叶斯网络融合中依赖保留与计算易处理性的平衡问题，提出带定制化设计的遗传算法，实验显示其性能优于适配方法与贪心基线。
AI中文摘要：
贝叶斯网络（BN）融合将多个输入网络合并为单一结构，在依赖关系保留与计算易处理性之间取得平衡。无约束融合保留所有依赖关系，但常生成树宽过高的复杂网络，影响推理可扩展性；受限融合通过剪枝边控制树宽，却易过拟合输入特定噪声并遗漏原始BN的依赖关系。本文提出一种共识框架，优先保留输入网络间的共享结构，同时施加树宽约束以保证良好共识；提出带高级初始化、专用算子及定制适应度函数的遗传算法，还将现有方法适配至该问题，并实现贪心基线用于基准测试与进一步优化。在合成及真实世界BN上的实验表明，所提遗传算法优于适配方法与贪心基线。
英文摘要：
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.
