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Abstract: In this paper, we illustrate that increasing coverage through diversity is not enough to ensure increased prediction accuracy---if the integration method does not utilize the coverage, then no benefit arises from integrating multiple models. We compare four criteria for selecting base-level classifiers and demonstrate that informed selection can lead to more accurate meta-level classifiers than random selection. In addition, we illustrate empirically that straightforward integration methods... (Update)
Context of citations to this paper: More
.... error as the fraction of instances for which a pair of base classifiers make the same incorrect predictions and Brodley and Lane [5] measured coverage by computing the fraction of instances for which at least one of the base classifiers produces the correct prediction....
...and by applying the same learning program on each of those training subsets (see Chapter 2) 5.2. 2 Coverage Brodley and Lane [ Brodley Lane, 1996 ] defined as coverage the fraction of instances for which at least one of the classifiers produces the correct prediction. Under...
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BibTeX entry: (Update)
C. Brodley and T. Lane. Creating and exploiting coverage and diversity. In Work. Notes AAAI-96 Workshop Integrating Multiple Learned Models, pages 8--14, 1996. http://citeseer.ist.psu.edu/brodley96creating.html More
@misc{ brodley96creating,
author = "C. Brodley and T. Lane",
title = "Creating and exploiting coverage and diversity",
text = "C. Brodley and T. Lane. Creating and exploiting coverage and diversity.
In Work. Notes AAAI-96 Workshop Integrating Multiple Learned Models, pages
8--14, 1996.",
year = "1996",
url = "citeseer.ist.psu.edu/brodley96creating.html" }
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