Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@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",
URL = "
http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2096&spage=68",
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