Time and Sample Efficient Discovery of Markov Blankets And Direct Causal Relations (2003)
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| Venue: | Proceedings of the 9th CAN SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Citations: | 21 - 5 self |
BibTeX
@INPROCEEDINGS{Tsamardinos03timeand,
author = {Ioannis Tsamardinos and Constantin Aliferis and Alexander Statnikov},
title = {Time and Sample Efficient Discovery of Markov Blankets And Direct Causal Relations},
booktitle = {Proceedings of the 9th CAN SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2003},
pages = {673--678}
}
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Abstract
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 mini- mal 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.
