智能系统学报2012，Vol.7Issue(2)：153-160,8.DOI:10.3969/j.issn.1673-4785.201112005
逻辑回归分析的马尔可夫毯学习算法
An algorithm for a Markov blanket based on logistic regression analysis
摘要
Abstract
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 min 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-min 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 C2 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.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Key words
Bayesian networks/ Markov blanket/ logistic regression analysis/ conditional independence test分类
信息技术与安全科学引用本文复制引用
郭坤,王浩,姚宏亮,李俊照..逻辑回归分析的马尔可夫毯学习算法[J].智能系统学报,2012,7(2):153-160,8.基金项目
国家自然科学基金资助项目(61070131,61175051). （61070131,61175051）
