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References (4)
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C. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, X. Koutsoukos (2010)
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and ExtensionsJ. Mach. Learn. Res., 11
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M. Scutari (2009)
Learning Bayesian Networks with the bnlearn R PackageJournal of Statistical Software, 35
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C. Aliferis, A. Statnikov, I. Tsamardinos, S. Mani, X. Koutsoukos (2010)
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical EvaluationJ. Mach. Learn. Res., 11
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I. Tsamardinos, Laura Brown, C. Aliferis (2006)
The max-min hill-climbing Bayesian network structure learning algorithmMachine Learning, 65
- Publisher
- Springer International Publishing
- Copyright
- © The Author(s) 2015
- ISBN
- 978-3-319-14432-0
- Pages
- 23 –32
- DOI
- 10.1007/978-3-319-14433-7_3
- Publisher site
- See Chapter on Publisher Site
Abstract
[In this chapter we introduce HITON-PC, another local causal discovery algorithm for finding the parents and children of a given target variable. Similar to PC-simple, HITON-PC applies conditional independence tests to identify strong and persistent associations between variables, but with a different approach to pruning the search space for the tests. This chapter firstly presents the basic idea of HITON-PC, then the algorithm is described in detail and illustrated with a simple example. The time complexity and false discoveries of HITON-PC are also discussed, and the chapter ends with the introduction to a software tool containing the implementation of HITON-PC.]
Published: Mar 3, 2015
Keywords: High Dimensional Data; Target Variable; Priority Queue; Maximum Order; Independence Test