Efficient Markov Blanket Discovery and Its Application
提出了一种新的马尔可夫毯发现算法STMB，通过利用目标节点的配偶与后代共存特性，省去了现有算法中的对称性约束，大幅减少独立性测试次数，在保持准确率的同时显著提升效率，适用于特征选择和贝叶斯网络结构学习。
In a Bayesian network (BN), a target node is independent of all other nodes given its Markov blanket (MB), and finding the MB has many applications, including feature selection and BN structure learning. We propose a new MB discovery algorithm, simultaneous MB (STMB), to improve the efficiency of the existing topology-based MB discovery algorithms. The proposed method removes the necessity of enforcing the symmetry constraint that is prevalent in existing algorithms, by exploiting the coexisting property between spouses and descendants of the target node. Since STMB mainly reduces the number of independence tests needed to complete the MB set after finding the parents-and-children set, it is applicable to all previous topology-based methods. STMB is both sound and complete. Experiments show that STMB has a comparable accuracy but much better efficiency than state-of-the-art methods. An application on benchmark feature selection datasets further demonstrates the excellent performance of STMB.