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Abstract: Distributed data mining systems aim to discover and combine useful information that is distributed across multiple databases. One of the main challenges is the design of effective and efficient methods to combine multiple models computed over multiple distributed sources that scale well over many large distributed databases. We describe in detail several methods that evaluate, prune and combine large collections of imported models computed at remote sites into efficient and scalable... (Update)
Context of citations to this paper: More
...are least correlated (and hence the least trusted) to the unpruned meta classifier. Both post training pruning algorithms are detailed in [54]. There are two primary objectives for the pruning techniques: 1. to acquire and combine information from multiple databases in a timely...
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BibTeX entry: (Update)
A. L. Prodromidis, S. J. Stolfo, and P. K. Chan. Effective and efficient pruning of metaclassifiers in a distributed data mining system. Technical report, Columbia Univ., 1999. CUCS017 -99. 34 http://citeseer.ist.psu.edu/prodromidis99effective.html More
@misc{ prodromidis99effective,
author = "A. Prodromidis and S. Stolfo and P. Chan",
title = "Effective and efficient pruning of metaclassifiers in a distributed data
mining system",
text = "A. L. Prodromidis, S. J. Stolfo, and P. K. Chan. Effective and efficient
pruning of metaclassifiers in a distributed data mining system. Technical
report, Columbia Univ., 1999. CUCS017 -99. 34",
year = "1999",
url = "citeseer.ist.psu.edu/prodromidis99effective.html" }
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