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A Markov Blanket Based Strategy to Optimize the Induction of Bayesian Classifiers When Using Conditional Independence Learning Algorithms

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Data Warehousing and Knowledge Discovery (DaWaK 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4654))

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Abstract

A Bayesian Network (BN) is a multivariate joint probability distribution graphical representation that can be induced from data. The induction of a BN is a NP problem. Two main approaches can be used for inducing a BN from data, namely, Conditional Independence (CI) and the Heuristic Search (HS) based algorithms. When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at an increase in computational efficiency. In this paper a new CI based algorithm (MarkovPC) to induce BCs from data is proposed. MarkovPC uses the Markov Blanket concept in order to impose some constraints and optimize the traditional PC algorithm. Experiments performed with ALARM BN, as well as other UCI and artificial domains revealed that MarkovPC tends to execute fewer comparisons than the traditional PC. The experiments also show that the MarkovPC produces competitive classification rates when compared with both, PC and Naïve Bayes.

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Il Yeal Song Johann Eder Tho Manh Nguyen

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© 2007 Springer-Verlag Berlin Heidelberg

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de O. Galvão, S.D.C., Hruschka, E.R. (2007). A Markov Blanket Based Strategy to Optimize the Induction of Bayesian Classifiers When Using Conditional Independence Learning Algorithms. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_33

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  • DOI: https://doi.org/10.1007/978-3-540-74553-2_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74552-5

  • Online ISBN: 978-3-540-74553-2

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