An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

References

• Mamdani , E.H. and Assilian , S. 1975 . An experiment in linguistic synthesis with fuzzy logic controller . International Journal of Man-Machine Studies , 7 : 1 – 13 .
   Google Scholar
• Takagi , T. and Sugeno , M. 1985 . Fuzzy identification of systems and its application to modeling and control . IEEE Transactions on Systems, Man and Cybernetics , 15 : 116 – 132 .
   Web of Science ®Google Scholar
• E. Aguirre , A. González , Raúl Pérez , “ An inductive approach for learning fuzzy relational rules ,” Proceedings Third EUSFLAT 2003 Conference , 88 – 93 2003 .
   Google Scholar
• Gaweda , A. E. and Zurada , J.M. 2003 . Data-driven linguistic modeling using relational fuzzy rules . IEEE Transactions of Fuzzy Systems , 11 ( 1 ) : 121 – 134 .
   Web of Science ®Google Scholar
• Yager , R.R. 1991 . The representation of fuzzy relational production rules . Journal of Applied Intelligence , 1 : 35 – 42 .
   Google Scholar
• González , A. and Pérez , R. 1998 . Completeness and consistency conditions for learning fuzzy rules . Fuzzy Sets and Systems , 96 : 37 – 51 .
   Web of Science ®Google Scholar
• Y. Caises , E. Leyva , A. González , R. Pérez , “ A Genetic Learning of Fuzzy Relational Rules ,” Proceedings of the IWCCI 2010, EEE World Conference on Computational Intelligence-FUZZ-IEEE , 207 – 214 2010 .
   Google Scholar
• González , A. and Pérez , R. 2009 . Improving the genetic algorithm of SLAVE . Mathware and Soft Computing , 16 : 59 – 70 .
   Google Scholar
• Yager , R.R. and Filev , D. P. 1996 . Relational partitioning of fuzzy rules . Fuzzy Sets and Systems , 80 : 57 – 69 .
   Web of Science ®Google Scholar
• T. Mitchell “Machine Learning,” Ed. 1997 MacGraw-Hill .
   Google Scholar
• Michalski , R.S. 1986 . “Understanding the nature of learning,” Machine Learning: An artificial intelligence approach , Vol. II , San Mateo , CA : Morgan Kaufmann .
   Google Scholar
• A. González , R. Pérez , “ An analysis of the scalability of an embedded feature selection model for classification problems ,” Proceedings of the Information Processing and Management of Uncertainty on Knowledge-Based Systems IPMU 2006 , 1949 – 1956 2006 .
   Google Scholar
• González , A. and Pérez , R. 2001 . Selection of Relevant Features in a Fuzzy Genetic Learning Algorithm . IEEE Transactions on System, Man and Cybernetics Part B , 31 ( 3 ) : 417 – 425 .
   PubMed Web of Science ®Google Scholar
• Kullback , S. 1978 . Information Theory and Statistics , Gloucester, Mass .
   Google Scholar
• L. Wehenkel , “ On uncertainty measures used for decision tree induction ,” Proc. of IPMU ’ 96 , 413 – 418 1996 .
   Google Scholar
• A. Asuncion , D.J. Newman , “ UCI Machine Learning Repository ” [ http://www.ics.uci.edu/mlearn/MLRepository.html ]. University of California, School of Information and Computer Science , Irvine , CA , 2007 .
   Google Scholar
• González , A. and Pérez , R. 1999 . SLAVE: to genetic learning system based on an iterative approach . IEEE Transaction on Fuzzy System , 27 ( 2 ) : 176 – 19 .
   Google Scholar
• O. Dunn ,“ Multiple Comparisons among means ,” Journal of the American Statistical Association , 1961 .
   PubMed Web of Science ®Google Scholar
• Witten , I.H. and Frank , E. 2005 . Data mining: practical machine learning tools and techniques , 2nd edition , San Francisco : Morgan Kaufmann .
   Google Scholar
• Demsar , J. 2006 . Statistical comparisons of classifiers over multiple data sets . Journal of Machine Learning Research , 7 : 1 – 30 .
   Web of Science ®Google Scholar
• M. Friedman , The use of ranks to avoid the assumption of normality implicit in the analysis of variance , Journal of the American Statistical Association , vol. 32 , no. 200 , 1937 .
   Google Scholar
• Quinlan , J.R. 1993 . C4.5 Program for Machine Learning , Morgan Kaufmann .
   Google Scholar
• Aha , D.W. , Kibler , D.F and Albert , M.K. 1991 . Instance-based learning algorithms. Machine Learning , 6 : 37 – 66 .
   Web of Science ®Google Scholar
• G.H. John and P. Langley , “ Estimating continuous distributions in bayesian classifiers ,” 11th Conference on Uncertainty in Artificial Intelligence , Morgan Kauf-mann , 338 – 345 1995 .
   Google Scholar
• E. Frank and I.H. Witten , “ Generating accurate rule sets without global optimization ,” Proceedings of the 15th International Conference on Machine Learning , Morgan Kaufmann , 144 – 151 1998 .
   Google Scholar
• Platt , J. 1998 . Fast training of support vector machines using sequential minimal optimization, Advances in Kernel Methods-Support Vector Machine , MIT Press .
   Google Scholar
• Alcalá , J. 2009 . KEEL: “A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems” . Soft Computing , 13 : 307 – 318 .
   Web of Science ®Google Scholar
• Ishibuchi , H. 1999 . Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems . IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics , 29 ( 5 ) : 601 – 618 .
   PubMed Web of Science ®Google Scholar
• Sánchez , L. and Couso , I. 2001 . Combining GP Operators With SA Search To Evolve Fuzzy Rule Based Classifiers . Information Sciences , 136 ( 1-4 ) : 175 – 192 .
   Web of Science ®Google Scholar
• Otero , J. and Sánchez , L. 2006 . Induction of descriptive fuzzy classifiers with the Logitboost algorithm . Soft Computing , 10 ( 9 ) : 825 – 835 .
   Web of Science ®Google Scholar