- author = "W. B. Langdon and S. J. Barrett and B. F. Buxton",
- title = "Genetic Programming for Combining Neural Networks for Drug Discovery",
- booktitle = "Soft Computing and Industry Recent Applications",
- year = "2001",
- editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and Takeshi Furuhashi and Frank Hoffmann",
- pages = "597--608",
- month = "10--24 " # sep,
- publisher = "Springer-Verlag",
- note = "Published 2002",
- keywords = "genetic algorithms, genetic programming, data fusion, data mining, knowledge discovery, Receiver Operating Characteristics, ensemble of classifiers, size fair crossover",
- ISBN = "1-85233-539-4",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_wsc6.pdf",
-
URL = "
https://link.springer.com/book/10.1007/978-1-4471-0123-9",
-
URL = "http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394",
-
DOI = "
doi:10.1007/978-1-4471-0123-9_51",
- abstract = "We have previously shown on a range of benchmarks \cite{langdon:2001:gROC} Genetic programming (GP) can automatically fuse given classifers of diverse types to produce a combined classifer whose Receiver Operating Characteristics (ROC) are better than \cite{scott:1998:BMVC}'s 'Maximum Realisable Receiver Operating Characteristics' (MRROC). I.e. better than their convex hull. Here our technique is used in a blind trial where artifcial neural networks ANN. are trained by Clementine on P450 pharmaceutical data. Using just the networks GP automatically evolves a composite classifer.",
-
notes = "March 2020 hard copy out of print. Available as
softcover.
",
