Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection
نویسندگان
چکیده
Markov Blankets discovery algorithms are important for learning a Bayesian network structure. We present an argument that tree ensemble masking measures can provide an approximate Markov blanket. Then an ensemble feature selection method is used to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). We compare our algorithm in the causal structure learning problem to other well-known feature selection methods, and to a Bayesian local structure learning algorithm.
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