@misc{10481/77891,
year = {2006},
url = {https://hdl.handle.net/10481/77891},
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.},
organization = {Spanish Ministry of Science and Technology TIN-2005-08386-C05-03 and TIN-2005-08386-C05-01},
publisher = {Springer},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems},
author = {Berlanga, F. J. and Gacto Colorado, María José and Herrera Triguero, Francisco},
}