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逻辑回归分析的马尔可夫毯学习算法 被引量:2

An algorithm for a Markov blanket based on logistic regression analysis
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摘要 针对当前的马尔可夫毯学习算法会引入不正确的父子节点和配偶节点的问题,提出了一种基于逻辑回归分析的马尔可夫毯学习算法RA-MMMB.利用MMMB算法得到候选的马尔可夫毯,建立目标变量与候选马尔可夫毯的逻辑回归方程,通过回归分析在保留与目标变量相关性很强的变量的同时,去掉MMMB等算法所引入的弱相关性的错误变量以及其他的弱相关性变量;然后利用G2测试去掉回归分析后候选马尔可夫毯中的兄弟节点,得到目标变量的马尔可夫毯.RA-MMMB算法通过回归分析,减少了条件独立测试的次数,提高了学习的精度.实验比较和分析表明,RA-MMMB算法能有效地发现变量的马尔可夫毯. To solve the problem of incorrect parent, child, and spouse nodes being brought into the current algorithms, an improved algorithm called a regression analysis-max rain Markov blanket (RA-MMMB) was presented u- sing the Markov Blanket based on logistic regression analysis. First, a logistic regression equation was established between the target variable and a set of its candidate Markov blankets obtained from the max-rain Markov blanket (MMMB) algorithm. Regression analysis can retain the variables strongly correlated with the target variable, and can remove the error variables and other variables weakly correlated with it as well. The incorrect nodes in the MMMB algorithm were also removed from the candidate Markov blanket; then, after the G2 conditiond independence test, which removed the brother node of the target variable in the candidate Markov blanket, returned after the regression analysis, the Markov blanket of the target variable was obtained. By the method of regression analysis, the RA-MMMB algorithm reduces the number of condition tests of independence and improves the accuracy of discovering the Markov blanket for the target variable. The result shows that the method can discover the Markov blanket of the target variable efficiently.
出处 《智能系统学报》 北大核心 2012年第2期153-160,共8页 CAAI Transactions on Intelligent Systems
基金 国家自然科学基金资助项目(61070131 61175051)
关键词 贝叶斯网络 马尔可夫毯 逻辑回归分析 条件独立测试 Bayesian networks Markov blanket logistic regression analysis conditional independence test
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