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KDD

2003

ACM

2003

ACM

Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, for every variable T in the network a special subset (Markov Blanket) identifiable by the network is the minimal variable set required to predict T. However, all known algorithms learning a complete BN do not scale up beyond a few hundred variables. On the other hand, all known sound algorithms learning a local region of the network require an exponential number of training instances to the size of the learned region. The contribution of this paper is two-fold. We introduce a novel local algorithm that returns all variables with direct edges to and from a target variable T as well as a local algorithm that returns the Markov Blanket of T. Both algorithms (i) are sound, (ii) can be run efficiently in datasets with thousands of variables, and (iii) significantly outperform in terms of approximating the true neig...

Added |
30 Nov 2009 |

Updated |
30 Nov 2009 |

Type |
Conference |

Year |
2003 |

Where |
KDD |

Authors |
Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov |

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