Abstract
Discovering the Markov blanket of a given variable can be viewed as a solution for optimal feature subset selection. Since 1996, several algorithms have been proposed to do local search of the Markov blanket, and they are proved to be much more efficient than the traditional approach where the whole Bayesian Network has to be learned first. In this paper, we compare those known published algorithms, including KS, GS, IAMB and its variants, PCMB, and one newly proposed called BFMB. We analyze the theoretical basis and practical values of each algorithm with the aim that it will help applicants to determine which ones to take in their specific scenarios.
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Fu, S., Desmarais, M.C. (2008). Tradeoff Analysis of Different Markov Blanket Local Learning Approaches. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_51
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DOI: https://doi.org/10.1007/978-3-540-68125-0_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
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