在线结构方程模型下的因果发现的基于选择函数的超启发学
Yinglong Dang1, Xiaoguang Gao1, Zidong Wang1
1School of Electronic and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Biomimetics (Basel, Switzerland)
|June 26, 2024
概括
这项研究引入了一种用于因果发现的新型超启发式方法,增强了指向非循环图 (DAG) 学习. 该方法有效地将群体智能与结构先验相结合,以改进因果关系识别.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器学习 机器学习
- 因果推理的原因推理.
背景情况:
- 对认知至关重要的因果发现依赖于学习定向非循环图 (DAG).
- 现有的DAG学习的元启发算法往往需要特定域调整,并且缺乏通用性.
- 超启发式算法通过结合和优化多个启发式算法提供了一个有希望的替代方案.
研究的目的:
- 提出一个多种群的选择函数超启发式,用于在DAG中发现因果关系.
- 将结构先验和专家知识与群体智能相结合,以进行可靠的因果发现.
- 为了提高DAG学习算法的概括能力.
主要方法:
- 开发了一个多人群选择函数超启发式框架.
- 使用部分相关性分析确定了部分v结构,以作为结构先验.
- 一个线性结构方程模型 (SEM) 被用来指导小群智能方法.
- 通过部分相关性分析,搜索空间受到限制.
主要成果:
- 提出的超启发式方法在因果发现方面表现出有效性.
- 实验结果显示,与现有最先进的方法相比,其性能优越.
- 该方法在六个标准基准网络上得到了验证.
结论:
- 开发的超启发式有效地将集群智能与DAG学习的结构先验相结合.
- 这种方法为因果发现提供了一个强大的解决方案,优于以前的方法.
- 这些发现凸显了超启发学在推进因果推理技术方面的潜力.
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