Genetic Programming for Improved Receiver Operating Characteristics

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{langdon:2001:mcs,

• author = "W. B. Langdon and B. F. Buxton",
• title = "Genetic Programming for Improved Receiver Operating Characteristics",
• booktitle = "Second International Conference on Multiple Classifier System",
• year = "2001",
• editor = "Josef Kittler and Fabio Roli",
• volume = "2096",
• series = "LNCS",
• pages = "68--77",
• address = "Cambridge",
• month = "2-4 " # jul,
• publisher = "Springer Verlag",
• keywords = "genetic algorithms, genetic programming, data fusion, data mining, knowledge discovery, Receiver Operating Characteristics, ensemble of classifiers, size fair crossover, cost-sensitive, cost trade off, non-uniform penalty",
• ISBN = "3-540-42284-6",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_mcs2001.pdf",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_mcs2001.ps.gz",
• DOI = "doi:10.1007/3-540-48219-9_7",
• size = "10 pages",
• abstract = "Genetic programming (GP) can automatically fuse given classifiers to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than [Scott et al. 1998]'s \cite{scott:1998:BMVC} ``Maximum Realisable Receiver Operating Characteristics'' (MRROC). I.e. better than their convex hull. This is demonstrated on artificial, medical and satellite image processing bench marks.",
• notes = "http://www.diee.unica.it/mcs/ Technique in \cite{langdon:2001:gROC} used to combine different classifiers on trained on different data.",

}

Genetic Programming entries for William B Langdon Bernard Buxton

Citations