\bibitem{r30}
A. Cano, A. Zafra and S. Ventura, {\it An EP algorithm for learning highly interpretable classifiers},
11th Inter. Conf. on Intelligent Systems Design and Applications (ISDA), (2011), 325--330.
\bibitem{r26}
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{r27}
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{r28}
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{r21}
C. Cortes and V. Vapnik, {\it Support vector networks}, Machine Learning, {\bf 20} (2004).
\bibitem{r24}
J. Demsar, {\it Statistical comparisons of classifiers over multiple data sets}, Journal of Machine Learning Research, {\bf 7} (2006), 1--30.
\bibitem{r31}
S. M. Fakhrahmad and M. Z. Jahromi, {\it A new rule-weight learning method based on gradient descent},
Proc. of World Congress on Engineering, {\bf I} (2009).
\bibitem{r1}
G. Forman and I. Cohen, {\it Learning from little: comparison of classifiers given little training},
PKDD 2004, LNAI 3202, Springer-Verlag Berlin Heidelberg 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{r25}
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{r22}
C. Hsu and C. Lin, {\it A comparison of methods for multiclass support vector machines}, IEEE Transaction on neural networks, {\bf 13}\textbf{(2)} (2002).
\bibitem{r15}
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{r18}
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{r5}
H. Ishibuchi, T. Nakashima and M. Nii, {\it Classification and modelling 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{r29}
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{r17}
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{r14}
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{r19}
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{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{r16}
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{r32}
M. Taheri, H. Azad, K. Ziarati and R. Sanaye, {\it A quadratic margin-based model for weighting fuzzy classification rules inspired by support vector machines}, Iranian Journal of Fuzzy Systems,
{\bf 10}\textbf{(4)} (2013), 41--55.
\bibitem{r43}
{\it UCI machine learning repository, http://www.ics.uci.edu/~mlearn/databases.}
\bibitem{r20}
V. Vapnik, {\it The nature of statistical learning theory}, Springer Verlag, New York, 1995.
\bibitem{r42}
K. Q. Weinberger, J. C. Blitzer and L. K. Saul, {\it Distance metric learning for large margin nearest neighbor classification}, Advances in Neural Information Processing Systems (NIPS), {\bf 18} (2006), 1473--1480.
\bibitem{r23}
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{r41}
L. Xu, K. Crammer and D. Schuurmans, {\it Robust support vector machine training via convex outlier ablation}, In Proc. of the National Conf. on Artificial Intelligence, {\bf 21}\textbf{(1)} (2006), 536--542.
\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{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.
)