A Genetic-Programming-Based Approach for the Learning
of Compact Fuzzy Rule-Based Classification Systems
F. Berlanga, M. del Jesus, M. Gacto, and F. Herrera. Proceedings 8th International Conference on Artificial
Intelligence and Soft Computing ICAISC, volume 4029 of Lecture Notes on Artificial Intelligence (LNAI), page 182--191. Zakopane, Poland, Springer-Verlag, (June 2006)
DOI: doi:10.1007/11785231_20
Abstract
In the design of an interpretable fuzzy rule-based
classification system (FRBCS) the precision as much as
the simplicity of the extracted knowledge must be
considered as objectives. In any inductive learning
algorithm, when we deal with problems with a large
number of features, the exponential growth of the fuzzy
rule search space makes the learning process more
difficult. Moreover it leads to an FRBCS with a rule
base with a high cardinality. In this paper, we propose
a genetic-programming-based method for the learning of
an FRBCS, where disjunctive normal form (DNF) rules
compete and cooperate among themselves in order to
obtain an understandable and compact set of fuzzy
rules, which presents a good classification performance
with high dimensionality problems. This proposal uses a
token competition mechanism to maintain the diversity
of the population. The good results obtained with
several classification problems support our proposal.
%0 Conference Paper
%1 Berlanga:2006:ICAISC
%A Berlanga, F. J.
%A del Jesus, M. J.
%A Gacto, M. J.
%A Herrera, F.
%B Proceedings 8th International Conference on Artificial
Intelligence and Soft Computing ICAISC
%C Zakopane, Poland
%D 2006
%E Rutkowski, Leszek
%E Tadeusiewicz, Ryszard
%E Zadeh, Lotfi A.
%E Zurada, Jacek
%I Springer-Verlag
%K algorithms, genetic programming
%P 182--191
%R doi:10.1007/11785231_20
%T A Genetic-Programming-Based Approach for the Learning
of Compact Fuzzy Rule-Based Classification Systems
%V 4029
%X In the design of an interpretable fuzzy rule-based
classification system (FRBCS) the precision as much as
the simplicity of the extracted knowledge must be
considered as objectives. In any inductive learning
algorithm, when we deal with problems with a large
number of features, the exponential growth of the fuzzy
rule search space makes the learning process more
difficult. Moreover it leads to an FRBCS with a rule
base with a high cardinality. In this paper, we propose
a genetic-programming-based method for the learning of
an FRBCS, where disjunctive normal form (DNF) rules
compete and cooperate among themselves in order to
obtain an understandable and compact set of fuzzy
rules, which presents a good classification performance
with high dimensionality problems. This proposal uses a
token competition mechanism to maintain the diversity
of the population. The good results obtained with
several classification problems support our proposal.
%@ 3-540-35748-3
@inproceedings{Berlanga:2006:ICAISC,
abstract = {In the design of an interpretable fuzzy rule-based
classification system (FRBCS) the precision as much as
the simplicity of the extracted knowledge must be
considered as objectives. In any inductive learning
algorithm, when we deal with problems with a large
number of features, the exponential growth of the fuzzy
rule search space makes the learning process more
difficult. Moreover it leads to an FRBCS with a rule
base with a high cardinality. In this paper, we propose
a genetic-programming-based method for the learning of
an FRBCS, where disjunctive normal form (DNF) rules
compete and cooperate among themselves in order to
obtain an understandable and compact set of fuzzy
rules, which presents a good classification performance
with high dimensionality problems. This proposal uses a
token competition mechanism to maintain the diversity
of the population. The good results obtained with
several classification problems support our proposal.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Zakopane, Poland},
author = {Berlanga, F. J. and {del Jesus}, M. J. and Gacto, M. J. and Herrera, F.},
bibdate = {2006-07-05},
bibsource = {DBLP,
http://dblp.uni-trier.de/db/conf/icaisc/icaisc2006.html#BerlangaJGH06},
biburl = {https://www.bibsonomy.org/bibtex/216d6c7cc19580d3c8bb914cfcafb20c4/brazovayeye},
booktitle = {Proceedings 8th International Conference on Artificial
Intelligence and Soft Computing {ICAISC}},
doi = {doi:10.1007/11785231_20},
editor = {Rutkowski, Leszek and Tadeusiewicz, Ryszard and Zadeh, Lotfi A. and Zurada, Jacek},
interhash = {b77a1a487a614326abe5bde71d9ba6fc},
intrahash = {16d6c7cc19580d3c8bb914cfcafb20c4},
isbn = {3-540-35748-3},
keywords = {algorithms, genetic programming},
month = {June 25-29},
pages = {182--191},
publisher = {Springer-Verlag},
series = {Lecture Notes on Artificial Intelligence (LNAI)},
size = {10 pages},
timestamp = {2008-06-19T17:36:29.000+0200},
title = {A Genetic-Programming-Based Approach for the Learning
of Compact Fuzzy Rule-Based Classification Systems},
volume = 4029,
year = 2006
}