\bibitem{r31}
S. M. Chen,
{\it Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms},
IEEE Trans. on Fuzzy Systems,
{\bf 11}\textbf{(4)} (2003), 495--506.
\bibitem{r32}
S. M. Chen,
{\it A new weighted fuzzy rule interpolation method based on GA-based weights-learning techniques},
proced. of ICMLC,
{\bf 5} (2010), 2705-2711.
\bibitem{r33}
S. M. Chen,
{\it Weighted fuzzy rule interpolation based on GA-based weight-learning techniques},
IEEE Trans. on Fuzzy Systems,
{\bf 19}\textbf{(4)} (2011),729--744.
\bibitem{r6}
Z. Chi, H. Yan and T. Pham,
{\it Fuzzy algorithms: with applications to image processing and pattern recognition},
World Scientific, Singapore, 1996.
\bibitem{r23}
O. Cordon, M. J. Del Jesus and F. Herrera,
{\it A proposal on reasoning methods in fuzzy rule-based classification systems},
Internat. J. Approx. Reason,
{\bf 20} (1999), 21--45.
\bibitem{r15}
O. Cordon, F. Gomide, F. Herrera, F. Hoffmann and L. Magdalena,
{\it Ten years of genetic fuzzy systems: current framework and new trends},
Fuzzy Sets and Systems,
{\bf 141} (2004), 5--31.
\bibitem{r25}
C. Cortes and V. Vapnik,
{\it Support vector networks},
Machine Learning,
{\bf 20} (2004).
\bibitem{r29}
J. Demsar,
{\it Statistical comparisons of classifiers over multiple data sets},
Journal of Machine Learning Research,
{\bf 7} (2006), 1--30.
\bibitem{r1}
G. Forman and I. Cohen,
{\it Learning from Little: Comparison of Classifiers Given Little Training},
Springer-Verlag, PKDD 2004, (2004), 161--172.
\bibitem{r11}
L. Fu,
{\it Rule generation from neural networks},
IEEE Transaction on systems, Man, and Cybernetics,
{\bf 24}\textbf{(8)} (1994).
\bibitem{r30}
S. Garcia and F. Herrera,
{\it An extension on statistical comparison of classifiers over multiple data sets for all pair wise comparisons},
Journal of Machine Learning Research,
{\bf 9} (2008), 2677--2694.
\bibitem{r14}
H. B. Gurocak and A. de Sam Lazaro,
{\it A fine tuning method for fuzzy logic rule bases},
Elsevier, Fuzzy Sets and Systems,
{\bf 67} (1994), 147--161.
\bibitem{r26}
C. W. Hsu and C. J. Lin,
{\it A comparison of methods for multiclass support vector machines},
IEEE Transaction on neural networks,
{\bf 13}\textbf{(2)} (2002).
\bibitem{r17}
Q. Hu, P. Zhu, Y. Yang and D. Yu,
{\it Large-margin nearest neighbor classifiers via sample weight learning},
Neurocomputing,{\bf 74}\textbf{(4)} (2011), 656--660.
\bibitem{r20}
H. Ishibuchi, T. Murata and I. B. Turksen,
{\it Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems},
Fuzzy Sets and Systems,
{\bf 89}\textbf{(2)} (1997), 135--150.
\bibitem{r9}
H. Ishibuchi and T. Nakashima,
{\it Effect of Rule Weights in Fuzzy Rule-based Classification Systems},
IEEE Transactions on Fuzzy Systems,
{\bf 9}\textbf{(4)} (2001), 506--515.
\bibitem{r28}
H. Ishibuchi, T. Nakashima and T. Morisawa,
{\it Simple fuzzy rule-based classification systems perform well on commonly used real-world data sets Fuzzy Information Processing Society},
NAFIPS '97., 1997 Annual Meeting of the North American,
(1997), 251--256.
\bibitem{r22}
H. Ishibuchi, T. Nakashima and T. Morisawa,
{\it Voting in Fuzzy Rule-based Systems for Pattern Classification Problems},
Fuzzy Sets and Systems,
{\bf 103}\textbf{(2)} (1999), 223--238.
\bibitem{r5}
H. Ishibuchi, T. Nakashima and M. Nii,
{\it Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining},
Springer Verlag, 2004.
\bibitem{r12}
H. Ishibuchi and M. Nii,
{\it Techniques and applications of neural networks for fuzzy rule approximation},
Fuzzy Theory Systems, (1999), 1491--1519.
\bibitem{r4}
H. Ishibuchi and Y. Nojima,
{\it Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning},
International Journal of Approximate Reasoning,
{\bf 44}\textbf {(1)} (2007), 4--31.
\bibitem{r34}
H. Ishibuchi, K. Nozaki and H. Tanaka,
{\it Distributed representation of fuzzy rules and its application to pattern classification},
Fuzzy Sets and Systems,
{\bf 52}\textbf{(1)} (1992), 21--32.
\bibitem{r19}
H. Ishibuchi, K. Nozaki, N. Yamamoto and H. Tanaka,
{\it Selecting fuzzy if-then rules for classification problems using genetic algorithms},
IEEE Transactions on Fuzzy Systems,
{\bf 3}\textbf{(3)} (1995), 260--270.
\bibitem{r16}
H. Ishibuchi and T. Yamamoto,
{\it Rule weight specification in fuzzy rule-based classification systems},
IEEE Trans. on Fuzzy Systems,
{\bf 13}\textbf{(4)} (2005), 428--435.
\bibitem{r21}
H. Ishibuchi and T. Yamamoto,
{\it Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining},
Fuzzy Sets and Systems,
{\bf 141}\textbf{(1)} (2004), 59--88.
\bibitem{r2}
J. Langford,
{\it Tutorial on practical prediction theory for classification},
Journal of Machine Learning Research,
{\bf 6} (2005), 273--306.
\bibitem{Mansoori2}
E. G. Mansoori, M. J. Zolghadri and S. D. Katebi,
{\it Using distribution of data to enhance performance of fuzzy classification systems}
Iranian Journal of Fuzzy Systems,
{\bf 4}\textbf{(1)} (2007), 21--36.
\bibitem{Mansoori1}
E. G. Mansoori, M. J. Zolghadri, S. D. Katebi and H. Mohabatkar,
{\it Generating fuzzy rules for protein classification},
Iranian Journal of Fuzzy Systems,
{\bf 5}\textbf{(2)} (2008), 21--33.
\bibitem{r3}
R. Mikut, J. Jakel and L. Groll,
{\it Interpretability issues in data-based learning of fuzzy systems},
Elsevier, Fuzzy Sets and Systems,
{\bf 150} (2005), 179--197.
\bibitem{r10}
T. Nakashima, G. Schaefer, Y. Yokota and H. Ishibuchi,
{\it A weighted fuzzy classifier and its application to image processing tasks},
Fuzzy Sets and Systems,
{\bf 158}\textbf{(3)} (2007), 284--294.
\bibitem{r18}
K. Nozaki, H. Ishibuchi and H. Tanaka,
{\it Adaptive fuzzy rule-based classification systems},
IEEE Transactions on Fuzzy Systems,
{\bf 4}\textbf{(3)} (1996), 238--250.
\bibitem{r24}
V. Vapnik,
{\it The nature of statistical learning theory},
Springer Verlag, New York, 1995.
\bibitem{r27}
J. Weston and C. Watkins,
{\it Support vector machines for multi-class pattern recognition},
ESANN'1999 proceedings - European Symposium on Artificial Neural Networks,
Bruges (Belgium),
isbn:2-600049-9-X, (1999), 219--224.
\bibitem{r13}
L. Yu and J. Xiao,
{\it Trade-off between accuracy and interpretability: experience-oriented fuzzy modeling via reduced-set vectors}, Elsevier, Computers and Mathematics with Applications,
{\bf 57} (2009), 885--895.
\bibitem{r8}
M. J. Zolghadri and E. G. Mansoori,
{\it Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis},
Information Sciences,
{\bf 177}\textbf{(11)} (2007), 2307--2296.
\bibitem{r7}
M. J. Zolghadri and M. Taheri,
{\it A proposed method for learning rule weights in fuzzy rule-based classification systems},
Fuzzy Sets and Systems, {\bf 159} (2008), 449--459.