一种快速因果网络骨架学习算法

Fast causal network skeleton learning algorithm

针对传统因果网络结构学习算法难以适用于高维网络的问题,该文提出1种快速且适用于高维网络的因果网络骨架构建算法。采取基于最大依赖性、最小冗余度的互信息加速策略,在2个节点间找出2个因果节点集,在这2个因果节点集的并集内分别对节点进行条件独立性测试。真实数据实验表明,在对高维网络进行结构学习时,该文算法的时间复杂度优于传统算法;由于减少了条件独立性测试的次数,提高了网络识别的准确率。

Aiming at the problem that the traditional structure of causal network learning algorithms is unfit for high dimensional networks ,a fast causal network skeleton construction algorithm is proposed for high dimensional networks .A mutual information accelerating strategy is used based on the maximum dependence and the minimum redundancy , two candidate causal sets between two nodes are found out ,and conditional independence tests of the 2 nodes are proposed in the union of the 2 candidate causal sets .Real data experiments show that the time complexity of the algorithm proposed here is better than the traditional algorithms for high dimensional networks;the recognition accuracy of networks is improved because conditional independence tests are decreased .