Abstract
We have previously shown [Langdon and Buxton, 2001b] on a range of benchmarks 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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Langdon, W.B., Barrett, S.J., Buxton, B.F. (2002). Genetic Programming for Combining Neural Networks for Drug Discovery. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_51
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DOI: https://doi.org/10.1007/978-1-4471-0123-9_51
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