TY - JOUR
T1 - Fuzzy-UCS
T2 - A Michigan-style learning fuzzy-classifier system for supervised learning
AU - Orriols-Puig, Albert
AU - Casillas, Jorge
AU - Bernadó-Mansilla, Ester
PY - 2009
Y1 - 2009
N2 - This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System specifically designed for supervised learning tasks. Fuzzy-UCS is inspired by UCS, an on-line accuracy-based Learning Classifier System. Fuzzy-UCS introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining similar performance and generalization capabilities to those presented by UCS. The behavior of Fuzzy-UCS is analyzed in detail from several perspectives. The granularity of the linguistic fuzzy representation to define complex decision boundaries is illustrated graphically, and the test performance obtained with different inference schemes is studied. Fuzzy-UCS is also compared with a large set of other fuzzy and nonfuzzy learners, demonstrating the competitiveness of its on-line architecture in terms of performance and interpretability. Finally, the paper shows the advantages obtained when Fuzzy-UCS is applied to learn fuzzy models from large volumes of data.
AB - This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System specifically designed for supervised learning tasks. Fuzzy-UCS is inspired by UCS, an on-line accuracy-based Learning Classifier System. Fuzzy-UCS introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining similar performance and generalization capabilities to those presented by UCS. The behavior of Fuzzy-UCS is analyzed in detail from several perspectives. The granularity of the linguistic fuzzy representation to define complex decision boundaries is illustrated graphically, and the test performance obtained with different inference schemes is studied. Fuzzy-UCS is also compared with a large set of other fuzzy and nonfuzzy learners, demonstrating the competitiveness of its on-line architecture in terms of performance and interpretability. Finally, the paper shows the advantages obtained when Fuzzy-UCS is applied to learn fuzzy models from large volumes of data.
KW - Genetic fuzzy systems
KW - Michigan-style learning classifier systems
KW - Pattern classification
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=67349179627&partnerID=8YFLogxK
U2 - 10.1109/TEVC.2008.925144
DO - 10.1109/TEVC.2008.925144
M3 - Article
AN - SCOPUS:67349179627
SN - 1089-778X
VL - 13
SP - 260
EP - 283
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
IS - 2
ER -