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Algorithms for Discovery of Multiple Markov Boundaries This publication appears in: Journal of Machine Learning Research Authors: A. Statnikov, N. I. Lytkin, J. Lemeire and C. Aliferis Volume: 14 Pages: 499-566 Publication Date: Feb. 2013
Abstract: Algorithms for Markov boundary discovery from data constitute an important recent development
in machine learning, primarily because they offer a principled solution to the variable/feature selection
problem and give insight on local causal structure. Over the last decade many sound algorithms
have been proposed to identify a single Markov boundary of the response variable. Even though
faithful distributions and, more broadly, distributions that satisfy the intersection property always
have a single Markov boundary, other distributions/data sets may have multiple Markov boundaries
of the response variable. The latter distributions/data sets are common in practical data-analytic applications, and there are several reasons why it is important to induce multiple Markov boundaries
from such data. However, there are currently no sound and efficient algorithms that can accomplish
this task. This paper describes a family of algorithms TIE* that can discover all Markov boundaries
in a distribution. The broad applicability as well as efficiency of the new algorithmic family is
demonstrated in an extensive benchmarking study that involved comparison with 26 state-of-the-art
algorithms/variants in 15 data sets from a diversity of application domains. External Link.
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