@inproceedings{eb895cd36985438fb4a9f804b16a20db,

title = "Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption",

abstract = "We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Bayesian network. The algorithm is correct under the faithfulness assumption, scalable and data efficient. The last two properties are important because we aim to apply the algorithm to identify the minimal set of random variables that is relevant for probabilistic classification in databases with many random variables but few instances. We report experiments with synthetic and real databases with 37, 441 and 139352 random variables showing that the algorithm performs satisfactorily.",

author = "Pe{\~n}a, {Jose M.} and Johan Bj{\"o}rkegren and Jesper Tegner",

year = "2005",

month = dec,

day = "1",

language = "English (US)",

isbn = "3540273263",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "136--147",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2005 ; Conference date: 06-07-2005 Through 08-07-2005",

}