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Abstract: We have previously shown on a range of benchmarks [ Langdon and Buxton, 2001b] genetic programming (GP) can automatically fuse given classifiers of diverse types to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than [Scott et al., 1998]'s "Maximum Realisable Receiver Operating Characteristics" (MRROC). I.e. better than their convex hull. Here our technique is used in a blind trial where artificial neural networks are trained by Clementine on P450 ... (Update)
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BibTeX entry: (Update)
@misc{ furuhashi-soft,
author = "Furuhashi And Homann",
title = "Soft Computing and Industry Recent Applications, R. Roy, M. Koppen, S.
Ovaska,",
url = "citeseer.ist.psu.edu/759817.html" }
Citations (may not include all citations):1053 Genetic Programming: On the Programming of Computers by Mean.. (context) - John, Koza - 1992
413 Adaptive mixtures of local experts (context) - Robert, Jacobs et al. - 1991
14 Multiple interacting programs: A representation for evolving.. - Peter, Angeline - 1998
14 Genetic programming for combining classifiers - Buxton, Langdon et al. - 2001
7 Evolving receiver operating characteristics for data fusion - Buxton, Langdon et al. - 2001
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