Identifying markov blankets with decision tree induction

Lewis Frey, Douglas Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander Statnikov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

The Markov Blanket of a target variable is the minimum conditioning set of variables that makes the target independent of all other variables. Markov Blankets inform feature selection, aid in causal discovery and serve as a basis for scalable methods of constructing Bayesian networks. This paper applies decision tree induction to the task of Markov Blanket identification. Notably, we compare (a) C5.0, a widely used algorithm for decision rule induction, (b) C5C, which post-processes C5.0's rule set to retain the most frequently referenced variables and (c) PC, a standard method for Bayesian Network induction. C5C performs as well as or better than C5.0 and PC across a number of data sets. Our modest variation of an inexpensive, accurate, off-the-shelf induction engine mitigates the need for specialized procedures, and establishes baseline performance against which specialized algorithms can be compared.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages59-66
Number of pages8
StatePublished - Dec 1 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
Country/TerritoryUnited States
CityMelbourne, FL
Period11/19/0311/22/03

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