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Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures

  • Eric V. StroblEmail author
  • Shyam Visweswaran
Chapter
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pass in order to be included in the conditioning set. As a result, these algorithms may not consider all possible conditional multivariate combinations. We improve on this limitation by proposing a backward elimination method that uses a kernel-based conditional dependence measure to identify the Markov blanket in a fully multivariate fashion. The algorithm is easy to implement and compares favorably to other methods on synthetic and real datasets.

Keywords

Feature ranking Markov blanket Machine learning 

Notes

Acknowledgements

We thank Dr. Subramani Mani for providing the U.S. Linked Infant Birth and Death 1991 dataset. This research was funded by the National Library of Medicine grant T15 LM007059-24 to the University of Pittsburgh Biomedical Informatics Training Program and the National Institute of General Medical Sciences grant T32 GM008208 to the University of Pittsburgh Medical Scientist Training Program.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghUSA

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