Distributed Data Mining: Why Do More Than Aggregating Models
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2645-2649.
Abstract
In this paper we deal with the problem of mining large distributed databases. We show that the aggregation of models, i.e., sets of disjoint classification rules, each built over a subdatabase is quite enough to get an aggregated model that is both predictive and descriptive, that presents excellent prediction capability and that is conceptually much simpler than the comparable techniques. These results are made possible by lifting the disjoint cover constraint on the aggregated model and by the use of a confidence coefficient associated with each rule in a weighted majority vote.
@inproceedings{ijcai2007p425,
title = {Distributed Data Mining: Why Do More Than Aggregating Models},
author = {Aounallah, Mohamed and Mineau, Guy W.},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2645--2649},
year = {2007},
url = {https://ijcai.org/proceedings/2007/425},
}
TY - CPAPER TI - Distributed Data Mining: Why Do More Than Aggregating Models AU - Aounallah, Mohamed AU - Mineau, Guy W. T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2645 EP - 2649 UR - https://ijcai.org/proceedings/2007/425 ER -Copied
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