Identifying Markov Blankets with Decision Tree Induction

Authors:
Lewis Frey;Douglas Fisher;Ioannis Tsamardinos;Constantin F. Aliferis;Alexander Statnikov
Affiliations:
-;-;-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

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Abstract

The Markov Blanket of a target variable is theminimum conditioning set of variables that makes thetarget independent of all other variables. MarkovBlankets inform feature selection, aid in causal discoveryand serve as a basis for scalable methods of constructingBayesian networks. This paper applies decision treeinduction to the task of Markov Blanket identification.Notably, we compare (a) C5.0, a widely used algorithmfor decision rule induction, (b) C5C, which post-processesC5.0's rule set to retain the most frequentlyreferenced variables and (c) PC, a standard method forBayesian Network induction. C5C performs as well as orbetter than C5.0 and PC across a number of data sets.Our modest variation of an inexpensive, accurate, off-the-shelfinduction engine mitigates the need for specializedprocedures, and establishes baseline performance againstwhich specialized algorithms can be compared.