Genetic Programming for Improved Receiver Operating
Characteristics
W. Langdon, and B. Buxton. Second International Conference on Multiple Classifier
System, volume 2096 of LNCS, page 68--77. Cambridge, Springer Verlag, (2-4 July 2001)
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 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.
%0 Conference Paper
%1 langdon:2001:mcs
%A Langdon, W. B.
%A Buxton, B. F.
%B Second International Conference on Multiple Classifier
System
%C Cambridge
%D 2001
%E Kittler, Josef
%E Roli, Fabio
%I Springer Verlag
%K Characteristics, Operating Receiver algorithms, classifiers, cost cost-sensitive, crossover, data discovery, ensemble fair fusion, genetic knowledge mining, non-uniform of off, penalty programming, size trade
%P 68--77
%T Genetic Programming for Improved Receiver Operating
Characteristics
%U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2096&spage=68
%V 2096
%X 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 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.
%@ 3-540-42284-6
@inproceedings{langdon:2001:mcs,
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 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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Cambridge},
author = {Langdon, W. B. and Buxton, B. F.},
biburl = {https://www.bibsonomy.org/bibtex/2d5c12e3375b0329b68e388afe7de740d/brazovayeye},
booktitle = {Second International Conference on Multiple Classifier
System},
editor = {Kittler, Josef and Roli, Fabio},
interhash = {11de8405d70e8d57320455a0b1b0e6a9},
intrahash = {d5c12e3375b0329b68e388afe7de740d},
isbn = {3-540-42284-6},
keywords = {Characteristics, Operating Receiver algorithms, classifiers, cost cost-sensitive, crossover, data discovery, ensemble fair fusion, genetic knowledge mining, non-uniform of off, penalty programming, size trade},
month = {2-4 July},
notes = {http://www.diee.unica.it/mcs/ Technique in
\cite{langdon:2001:gROC} used to combine different
classifiers on trained on different data.},
pages = {68--77},
publisher = {Springer Verlag},
series = {LNCS},
size = {10 pages},
timestamp = {2008-06-19T17:44:59.000+0200},
title = {Genetic Programming for Improved Receiver Operating
Characteristics},
url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2096&spage=68},
volume = 2096,
year = 2001
}