H. Mallinson, and P. Bentley. Computational Integration for Modelling, Control and
Automation '99, 1, Hotel Marriott, Vienna, Austria, IOS Press, (17-19 February 1999)
Abstract
This paper describes the use of a Hybrid Fuzzy-Genetic
Programming system to discover patterns in large
databases. It does this by evolving a series of
variablelength fuzzy rules which generalise from a
training set of labelled classes. Numerous novel
techniques, including the use of genotypes in Genetic
Programming, two new genetic crossover operators, and
the processes of Modal Evolution, Modal Reevolution and
Nested Evolutionary Search are described. Experimental
results show that the system is able to classify data
from the Wisconsin Breast Cancer database correctly 95%
of the time.
%0 Conference Paper
%1 MallBent99
%A Mallinson, Hugh
%A Bentley, Peter
%B Computational Integration for Modelling, Control and
Automation '99
%C Hotel Marriott, Vienna, Austria
%D 1999
%E Mohammadian, Masoud
%I IOS Press
%K algorithms, classification fuzzy genetic programming,
%T Evolving Fuzzy Rules for Pattern Classification
%U http://www.cs.ucl.ac.uk/staff/P.Bentley/MABEC1.pdf
%V 1
%X This paper describes the use of a Hybrid Fuzzy-Genetic
Programming system to discover patterns in large
databases. It does this by evolving a series of
variablelength fuzzy rules which generalise from a
training set of labelled classes. Numerous novel
techniques, including the use of genotypes in Genetic
Programming, two new genetic crossover operators, and
the processes of Modal Evolution, Modal Reevolution and
Nested Evolutionary Search are described. Experimental
results show that the system is able to classify data
from the Wisconsin Breast Cancer database correctly 95%
of the time.
%@ 90-5199-473-7
@inproceedings{MallBent99,
abstract = {This paper describes the use of a Hybrid Fuzzy-Genetic
Programming system to discover patterns in large
databases. It does this by evolving a series of
variablelength fuzzy rules which generalise from a
training set of labelled classes. Numerous novel
techniques, including the use of genotypes in Genetic
Programming, two new genetic crossover operators, and
the processes of Modal Evolution, Modal Reevolution and
Nested Evolutionary Search are described. Experimental
results show that the system is able to classify data
from the Wisconsin Breast Cancer database correctly 95%
of the time.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Hotel Marriott, Vienna, Austria},
author = {Mallinson, Hugh and Bentley, Peter},
biburl = {https://www.bibsonomy.org/bibtex/2bbac1fe1081350914f0d007985f13892/brazovayeye},
booktitle = {Computational Integration for Modelling, Control and
Automation '99},
editor = {Mohammadian, Masoud},
interhash = {50bcd09206237d972bf7873ab086a76c},
intrahash = {bbac1fe1081350914f0d007985f13892},
isbn = {90-5199-473-7},
keywords = {algorithms, classification fuzzy genetic programming,},
month = {17-19 February},
notes = {CIMCA'99
http://www.gscit.monash.edu.au/conferences/cimca99/
UCI Wisconsin Breast Cancer},
publisher = {IOS Press},
size = {8 pages},
timestamp = {2008-06-19T17:46:13.000+0200},
title = {Evolving Fuzzy Rules for Pattern Classification},
url = {http://www.cs.ucl.ac.uk/staff/P.Bentley/MABEC1.pdf},
volume = 1,
year = 1999
}