动态编程BN结构学习算法 在小样本条件下集成双重约束
Zhigang Lv1,2, Yiwei Chen2, Ruohai Di2
1School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了贝叶斯网络 (BN) 结构学习的双约束动态编程算法,提高了小数据集的准确性. 整合先前的知识显著提高了BN学习效率和精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于动态编程 (DP) 的贝叶斯网络 (BN) 结构学习可以实现全球最佳解决方案.
- 然而,基于DP的BN学习通常会产生不准确的结构,样本数据很小或不完整.
- 现有的方法在有限的样本条件下与已学习的BN结构的可靠性作斗争.
研究的目的:
- 提出一个新的动态编程贝叶斯网络结构学习算法,用于小样本场景的双重约束.
- 通过解决小样本大小的局限性,提高已学习的BN结构的准确性和可靠性.
- 调查整合先前知识对BN结构学习的表现的影响.
主要方法:
- 开发了一个具有边缘和路径约束的动态编程BN结构学习算法.
- 双重约束被用来限制DP规划过程并减少搜索空间.
- 整合了双重约束来指导最佳父节点的选择,确保与先前知识保持一致.
主要成果:
- 拟议的双约束算法有效地限制了DP规划空间和父节点选择.
- 模拟结果证明了算法的有效性在小样本条件下.
- 与非整合方法相比,整合先前的知识显著提高了BN结构学习的效率和准确性.
结论:
- 双约束动态编程方法为贝叶斯网络结构学习提供了强大的解决方案,数据有限.
- 集成先前的知识对于提高BN结构学习算法的性能至关重要.
- 拟议的方法提供了一种更准确和更有效的方法来学习BN结构,特别是在处理小样本时.
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