Data mining with constrained-syntax genetic
programming: applications to medical data sets
C. Bojarczuk, H. Lopes, и A. Freitas. Proceedings Intelligent Data Analysis in Medicine and
Pharmacology (IDAMAP-2001), (2001)a workshop at MedInfo-2001.
Аннотация
This work is intended to discover classification rules
for diagnosing certain pathologies. In order to
discover these rules we have developed a new
constrained-syntax genetic programming algorithm based
on some concepts of data mining, particularly with
emphasis on the discovery of comprehensible knowledge.
We compare the performance of the proposed GP algorithm
with a genetic algorithm and with the very well-known
decision-tree algorithm C4.5.
Proceedings Intelligent Data Analysis in Medicine and
Pharmacology (IDAMAP-2001)
год
2001
notes
IDAMAP workshop
http://www.ailab.si/idamap/idamap2001/
Evolves IFTHEN rules. GP syntax contrained similar to
STGP. Size of rules used as component of fitness
function (actually product of sensitivity, specificity
and size releated coefficient. Demonstrated on 3 small
medical datasets (2 UCI).
%0 Conference Paper
%1 bojarczuk:2001:idamap
%A Bojarczuk, Celia C.
%A Lopes, Heitor S.
%A Freitas, Alex A.
%B Proceedings Intelligent Data Analysis in Medicine and
Pharmacology (IDAMAP-2001)
%D 2001
%K Constrained-Syntax Genetic Programming algorithms, applications, classification, data genetic medical mining, programming,
%T Data mining with constrained-syntax genetic
programming: applications to medical data sets
%U http://citeseer.ist.psu.edu/459555.html
%X This work is intended to discover classification rules
for diagnosing certain pathologies. In order to
discover these rules we have developed a new
constrained-syntax genetic programming algorithm based
on some concepts of data mining, particularly with
emphasis on the discovery of comprehensible knowledge.
We compare the performance of the proposed GP algorithm
with a genetic algorithm and with the very well-known
decision-tree algorithm C4.5.
@inproceedings{bojarczuk:2001:idamap,
abstract = {This work is intended to discover classification rules
for diagnosing certain pathologies. In order to
discover these rules we have developed a new
constrained-syntax genetic programming algorithm based
on some concepts of data mining, particularly with
emphasis on the discovery of comprehensible knowledge.
We compare the performance of the proposed GP algorithm
with a genetic algorithm and with the very well-known
decision-tree algorithm C4.5.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Bojarczuk, Celia C. and Lopes, Heitor S. and Freitas, Alex A.},
biburl = {https://www.bibsonomy.org/bibtex/293a16e6bc9c9aecb68bf3ae2cbf3c284/brazovayeye},
booktitle = {Proceedings Intelligent Data Analysis in Medicine and
Pharmacology (IDAMAP-2001)},
interhash = {e53557f9d64a68a7dfb26732f7d2159a},
intrahash = {93a16e6bc9c9aecb68bf3ae2cbf3c284},
keywords = {Constrained-Syntax Genetic Programming algorithms, applications, classification, data genetic medical mining, programming,},
note = {a workshop at MedInfo-2001},
notes = {IDAMAP workshop
http://www.ailab.si/idamap/idamap2001/
Evolves IFTHEN rules. GP syntax contrained similar to
STGP. Size of rules used as component of fitness
function (actually product of sensitivity, specificity
and size releated coefficient. Demonstrated on 3 small
medical datasets (2 UCI).},
timestamp = {2008-06-19T17:36:44.000+0200},
title = {Data mining with constrained-syntax genetic
programming: applications to medical data sets},
url = {http://citeseer.ist.psu.edu/459555.html},
year = 2001
}