A naïve approach for handling Markov blanket feature selection under non-faithful conditions involves first clustering all features into multiple clusters, and then randomly sampling a representative from each cluster.
Abstract—In faithful Bayesian networks, the Markov blanket of the class attribute is a unique and minimal feature subset for optimal feature selection.
In faithful Bayesian networks, the Markov blanket of the class attribute is a unique and minimal feature subset for optimal feature selection.
Markov Blanket Feature Selection with Non-faithful Data ... - dblp
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Kui Yu, Xindong Wu, Zan Zhang, Yang Mu, Hao Wang, Wei Ding : Markov Blanket Feature Selection with Non-faithful Data Distributions. ICDM 2013: 857-866.
Sep 5, 2016 · Abstract: It has received much attention in recent years to use Markov blankets in a Bayesian network for feature selection.
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A Markov Blanket of a random variable in a Bayesian network refers to a set of variables that, when instantiated, shields the variable from the influence of ...
In faithful Bayesian networks, the Markov blanket of the class attribute is a unique and minimal feature subset for optimal feature selection. However, little ...
The goal of this paper is to develop a fast algorithm for discovering Markov blankets from data. We emphasize that we do not address Bayesian network structure ...
In faithful Bayesian networks, the Markov blanket of the class attribute is a unique and minimal feature subset for optimal feature selection.
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We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON.