[1]GUO Kun,WANG Hao,YAO Hongliang,et al.An algorithm for a Markov blanket based on ?logistic regression analysis[J].CAAI Transactions on Intelligent Systems,2012,7(2):153-160.
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An algorithm for a Markov blanket based on ?logistic regression analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 2 Page number: 153-160 Column: 学术论文—机器学习 Public date: 2012-04-25
Title:
An algorithm for a Markov blanket based on ?logistic regression analysis
Author(s):
GUO Kun;  WANG Hao;  YAO Hongliang;  LI Junzhao
College of Computer and Information, Hefei University of Technology, Hefei 230009, China
Keywords:
Bayesian networks;  Markov blanket;  logistic regression analysis;  conditional independence test
CLC:
TP181
DOI:
-
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 using 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 G2 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.
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