马尔可夫毯
计算机科学
节点(物理)
水准点(测量)
独立性(概率论)
集合(抽象数据类型)
特征(语言学)
约束(计算机辅助设计)
贝叶斯网络
特征选择
马尔可夫链
拓扑(电路)
数据挖掘
算法
理论计算机科学
机器学习
马尔可夫模型
数学
马尔可夫性质
工程类
组合数学
统计
哲学
结构工程
语言学
程序设计语言
地理
大地测量学
几何学
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2017-05-01
卷期号:47 (5): 1169-1179
被引量:48
标识
DOI:10.1109/tcyb.2016.2539338
摘要
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.
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