Table of Contents




1.   OVERVIEW

Accuracy improvements to find the balance interpretability-accuracy      3
in linguistic fuzzy modeling: an overview
     J. Casillas, O. Cord´n, F. Herrera, L. Magdalena
                         o


2.   ACCURACY IMPROVEMENTS CONSTRAINED BY INTER-
     PRETABILITY CRITERIA

COR methodology: a simple way to obtain linguistic fuzzy models        27
with good interpretability and accuracy
    J. Casillas, O. Cord´n, F. Herrera
                        o

Constrained optimization of genetic fuzzy systems                      46
   F. Cheong, R. Lai

Trade-off between the number of fuzzy rules and their classification     72
performance
    H. Ishibuchi, T. Yamamoto

Generating distinguishable, complete, consistent and compact fuzzy    100
systems using evolutionary algorithms
    Y. Jin

Fuzzy CoCo: balancing accuracy and interpretability of fuzzy models   119
by means of coevolution
   C.A. Pe˜a-Reyes, M. Sipper
           n

On the achievement of both accurate and interpretable fuzzy systems   147
using data-driven design processes
    J. Valente de Oliveira, P. Fazendeiro
3.   EXTENDING THE MODELING PROCESS TO IMPROVE
     THE ACCURACY

Linguistic hedges and fuzzy rule based systems                           165
   C.-Y. Chen, B.-D. Liu

Automatic construction of fuzzy rule-based fuzzy systems: A trade-       193
off between complexity and accuracy maintaining interpretability
   H. Pomares, I. Rojas, J. Gonz´lez
                                 a

Using individually tested rules for the databased generation of inter-   220
pretable rule bases with high accuracy
    T. Slawinski, P. Krause, H. Kiendl


4.   EXTENDING THE MODEL STRUCTURE TO IMPROVE
     THE ACCURACY

A description of several characteristics for improving the accuracy      249
and interpretability of the fuzzy rule learning algorithms
   E. Aguirre, A. Gonz´lez, R. P´rez
                         a         e

An iterative learning methodology to design hierarchical systems of      277
linguistic rules for linguistic modeling
    R. Alcal´, O. Cord´n, F. Herrera, I. Zwir
             a           o

Learning default fuzzy rules with general and punctual exceptions        302
   P. Carmona, J.L. Castro, J.J. Castro-Schez, M. Laguia

Integration of fuzzy knowledge                                           338
    T.-P. Hong, C.-H. Wang, S.-S. Tseng

Tuning fuzzy partitions or assigning weights to fuzzy rules: which is    366
better?
    L. S´nchez, J. Otero
        a