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A Novel Method to Estimate Parents and Children for Local Bayesian Network Learning

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 295))

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Abstract

The Markov Blanket of a random variable is the minimum conditioning set of variables that makes the variable independent of all other variables. A core step to estimate the Markov Blanket is the identification of the Parents and Children (PC) variable set. This paper propose a novel Parents and Children discovery algorithm, called Max-Min Random Walk Parents and Children (MMRWPC), which improves the computational burden of the classical Max-Min Parents and Children method (MMPC). The improvement was achieved with a series of modifications, including the introduction of a random walk process to better identifying conditioning sets in the conditional independence (CI) tests, implying in a significantly reduction of expensive high-order CI tests. In a series of experiments with data sampled from benchmark Bayesian networks we show the suitability of the proposed method.

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Acknowledgment

The authors gratefully acknowledges financial support by INNOVATE PERU (Grant 334-INNOVATEPERU-BRI-2016).

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Correspondence to Edwin Villanueva .

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del Río, S., Villanueva, E. (2022). A Novel Method to Estimate Parents and Children for Local Bayesian Network Learning. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_35

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