Genetic Programming for Combining Neural Networks for Drug Discovery
نویسندگان
چکیده
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 pharmaceutical data. Using just the networks, GP automatically evolves a composite classifier.
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