A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

References

• Kuncheva , L. 2004 . Combining Pattern Classifiers: Methods and Algorithms , Wiley .
   Google Scholar
• Verzi , S.J. , Heileman , G.L. and Georgiopoulos , M. 2006 . Boosted ARTMAP: Modifications to fuzzy ARTMAP motivated by boosting theory . Neural Networks , 19 ( 4 ) : 446 – 468 .
   PubMedGoogle Scholar
• Pedrycz , W. and Kwak , K.C. 2006 . Boosting of granular models . Fuzzy Sets and Systems , 157 ( 22 ) : 2934 – 2953 .
   Google Scholar
• Takahashi , H. and Honda , H. 2005 . A new reliable cancer diagnosis method using boosted fuzzy classifier with a SWEEP operator method . Journal of Chemical Engineering of Japan , 38 ( 9 ) : 763 – 773 .
   Google Scholar
• Canul-Reich , J. , Shoemaker , L. and Hall , L.O. 2007 . “ Ensembles of fuzzy classifiers ” . In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , 1 – 6 . UK : London .
   Google Scholar
• Nojima , Y. and Ishibuchi , H. 2007 . Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design . International Journal of Hybrid Intelligent Systems , 4 ( 3 ) : 157 – 169 .
   Google Scholar
• C. Marsala . Data mining with ensembles of fuzzy decision trees . In IEEE Symposium on Computational Intelligence and Data Mining , 348 – 354 , 2009 Nashville USA .
   Google Scholar
• Bonissone , P. P. , Cadenas , J. M. , Garrido , M. C. and Díaz-Valladares , R. A. 2010 . A fuzzy random forest . International Journal of Approximate Reasoning , 51 ( 7 ) : 729 – 747 .
   Web of Science ®Google Scholar
• O. Cordón , A. Quirin , and L. Sánchez . A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers . In Third International Workshop on Genetic and Evolving Fuzzy Systems (GEFS) , pages 11 – 16 , Witten-Bommerholz Germany , 2008 .
   Google Scholar
• Cordón , O. and Quirin , A. 2010 . Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection . International Journal of Hybrid Intelligent Systems , 7 ( 1 ) : 45 – 64 .
   Google Scholar
• K. Trawiński , A. Quirin , and O. Cordón . Bi-criteria genetic selection of bagging fuzzy rule-based multiclassification systems . In IFSA/EUSFLAT Conf ., pages 1514 – 1519 , 2009 .
   Google Scholar
• Trawiński K. Quirin A. Cordón O. On the combination of accuracy and diversity measures for genetic selection of bagging fuzzy rule-based multi-classification systems International Conference on Intelligent Systems Design and Applications (ISDA) 2009 121 127
   Google Scholar
• Partridge , D. and Yates , W.B. 1996 . Engineering multi-version neural-net systems . Neural Computation , 8 ( 4 ) : 869 – 893 .
   PubMed Web of Science ®Google Scholar
• Cordón , O. , Herrera , F. , Hoffmann , F. and Magdalena , L. 2001 . Genetic Fuzzy Systems. Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , World Scientific .
   Google Scholar
• Cordón , O. , Gomide , F. , Herrera , F. , Hoffmann , F. and Magdalena , L. 2004 . Ten years of genetic fuzzy systems: Current framework and new trends . Fuzzy Sets and Systems , 141 ( 1 ) : 5 – 31 .
   Web of Science ®Google Scholar
• Herrera F. Genetic fuzzy systems: taxonomy, current research trends and prospects Evolutionary Intelligence 1 1 27 – 46 January 2008
   Google Scholar
• Trawiński , K. , Cordón , O. and Quirin , A. 2011 . On designing fuzzy multiclassifier systems by combining furia with bagging and feature selection, International Journal of Uncertainty . Fuzziness and Knowledge-Based Systems , 19 ( 4 ) : 589 – 633 .
   Web of Science ®Google Scholar
• Hühn , J. C. and Hüllermeier , E. 2009 . FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery , 19 ( 3 ) : 293 – 319 .
   Web of Science ®Google Scholar
• Hühn , J. C. and Hüllermeier , E. 2010 . An analysis of the FURIA algorithm for fuzzy rule induction. In Advances in Machine Learning I 321 – 344 .
   Google Scholar
• Ho , T. 1998 . The random subspace method for constructing decision forests . IEEE Transactions on Pattern Analysis and Machine Intelligence , 20 ( 8 ) : 832 – 844 .
   Web of Science ®Google Scholar
• Battiti , R. 1994 . Using mutual information for selecting features in supervised neural net learning . IEEE Transactions on Neural Networks , 5 ( 4 ) : 537 – 550 .
   PubMed Web of Science ®Google Scholar
• Feo , T.A. and Resende , M.G.C. 1995 . Greedy randomized adaptive search procedures . Journal of Global Optimization , 6 : 109 – 133 .
   Web of Science ®Google Scholar
• Ishibuchi , H. , Nakashima , T. and Nii , M. 2004 . Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing) , Secaucus , NJ, USA : Springer-Verlag New York, Inc .
   Google Scholar
• Coello , C.A. , Lamont , G.B. and Van Veldhuizen , D.A. 2007 . Evolutionary Algorithms for Solving Multi-Objective Problems , 2nd Edition , Springer .
   Google Scholar
• Ludmila , I. Kuncheva and Christopher , J. Whitaker . 2003 . Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy . Machine Learning , 51 ( 2 ) : 181 – 207 .
   Google Scholar
• Ruta , D. and Gabrys , B. 2005 . Classifier selection for majority voting . Information Fusion , 6 ( 1 ) : 63 – 81 .
   Google Scholar
• Tsymbal , A. , Pechenizkiy , M. and Cunningham , P. 2005 . Diversity in search strategies for ensemble feature selection . Information Fusion , 6 ( 1 ) : 83 – 98 .
   Google Scholar
• Deb , K. , Pratap , A. , Agarwal , S. and Meyarivan , T. 2002 . A fast and elitist multiobjective genetic algorithm: NSGA-II . IEEE Transactions on Evolutionary Computation , 6 : 182 – 197 .
   Web of Science ®Google Scholar
• E.M. Dos Santos , R. Sabourin , P. Maupin . Single and multi-objective genetic algorithms for the selection of ensemble of classifiers . In International Joint Conference on Neural Networks (IJCNN) , pages 3070 – 3077 , Vancouver , 2006 .
   Google Scholar
• Dos Santos , E.M. , Sabourin , R. and Maupin , P. 2008 . A dynamic overproduce-and-choose strategy for the selection of classifier ensembles . Pattern Recognition , 41 ( 10 ) : 2993 – 3009 .
   Google Scholar
• Dietterich , T.G. 2000 . An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization . Machine Learning , 40 ( 2 ) : 139 – 157 .
   Web of Science ®Google Scholar
• R.E . Banfield , L.O . Hall , K.W . Bowyer , W.P. Kegelmeyer . A comparison of decision tree ensemble creation techniques , IEEE Transactions on Pattern Analysis and Machine Intelligence , 29 1 : 173 – 180 , 2007 .
   PubMedGoogle Scholar
• Optiz , D. and Maclin , R. 1999 . Popular ensemble methods: an empirical study . Journal of Artificial Intelligence Research , 11 : 169 – 198 .
   Google Scholar
• Breiman , L. 1996 . Bagging predictors . Machine Learning , 24 ( 2 ) : 123 – 140 .
   Web of Science ®Google Scholar
• Schapire , R. 1990 . The strength of weak learnability . Machine Learning , 5 ( 2 ) : 197 – 227 .
   Web of Science ®Google Scholar
• Zhou , Z.H. 2005 . Ensembling local learners through multimodal perturbation . IEEE Transactions of Systems, Man, and Cybernetics Part B: Cybernetics , 354 : 725 – 735 .
   Google Scholar
• Xu , L. , Krzyzak , A. and Suen , C.Y. 1992 . Methods of combining multiple classifiers and their application to handwriting recognition . IEEE Transactions on Systems, Man and Cybernetics , 22 ( 3 ) : 418 – 435 .
   Web of Science ®Google Scholar
• Breiman , L. 2001 . Random forests . Machine Learning , 451 : 5 – 32 .
   Google Scholar
• del Jesus , M.J. , Hoffmann , F. , Navascues , L.J. and Sánchez , L. 2004 . Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms . IEEE Transactions on Fuzzy Systems , 12 ( 3 ) : 296 – 308 .
   Web of Science ®Google Scholar
• Sánchez , L. and Otero , J. 2007 . Boosting fuzzy rules in classification problems under single-winner inference . International Journal of Intelligent Systems , 229 : 1021 – 1034 .
   Google Scholar
• Takahashi , H. and Honda , H. 2006 . Lymphoma prognostication from expression profiling using a combination method of boosting and projective adaptive resonance theory . Journal of Chemical Engineering of Japan , 39 ( 7 ) : 767 – 771 .
   Google Scholar
• Janikow , C. Z. 1998 . Fuzzy decision trees: issues and methods . IEEE Transactions on Systems, Man, and Cybernetics Part B , 28 ( 1 ) : 1 – 14 .
   PubMed Web of Science ®Google Scholar
• J.J. Aguilera , M. Chica , M.J. del Jesus , and F. Herrera . Niching genetic feature selection algorithms applied to the design of fuzzy rule based classification systems . In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , pages 1794 – 1799 , London UK , 2007 .
   Google Scholar
• Y. Nojima , H. Ishibuchi Designing fuzzy ensemble classifiers by evolutionary multiobjective optimization with an entropy-based diversity criterion . In International Conference on Hybrid Intelligent Systems and Conference on Neuro-Computing and Evolving Intelligence , Auckland , New Zealand , 2006 .
   Google Scholar
• Ishibuchi , H. and Nojima , Y. 2006 . Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers . International Journal of Hybrid Intelligent Systems , 3 ( 3 ) : 129 – 145 .
   Google Scholar
• R. Schapire , Y. Freund , P. Bartlett , W. Lee . Boosting the margin: A new explanation for the effectiveness of voting methods . In International Conference on Machine Learning , pages 322 – 330 , Nashville USA , 1997.
   Google Scholar
• Dasarathy , B.V. and Sheela , B.V. 1979 . A composite classifier system design: Concepts and methodology . Proceedings of IEEE , 67 ( 5 ) : 708 – 713 .
   Google Scholar
• Oliveira , L.S. , Morita , M. , Sabourin , R. and Bortolozzi , F. 2005 . Multi-objective genetic algorithms to create ensemble of classifiers . Lecture Notes in Computer Science , 3410 : 592 – 606 .
   Google Scholar
• W. W. Cohen . Fast effective rule induction . In In Proceedings of the Twelfth International Conference on Machine Learning , pages 115 – 123 . Morgan Kaufmann , 1995 .
   Google Scholar
• Ishibuchi , H. , Nakashima , T. and Morisawa , T. 1999 . Voting in fuzzy rule-based systems for pattern classification problems . Fuzzy Sets and Systems , 103 ( 2 ) : 223 – 238 .
   Web of Science ®Google Scholar
• Cordón , O. , del Jesus , M.J. and Herrera , F. 1999 . A proposal on reasoning methods in fuzzy rule-based classification systems . International Journal of Approximate Reasoning , 20 : 21 – 45 .
   Web of Science ®Google Scholar
• P. Panov , S. Džeroski . Combining bagging and random subspaces to create better ensembles . In IDA'07: Proceedings of the 7th international conference on Intelligent data analysis , pages 118 – 129 , Berlin Heidelberg , 2007 . Springer-Verlag .
   Google Scholar
• Zitzler , E. and Thiele , L. 1999 . Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach . IEEE Transactions on Evolutionary Computation , 3 : 257 – 271 .
   Web of Science ®Google Scholar
• Quinlan , J. R. 1993 . C4.5: programs for machine learning , San Francisco , CA, USA : Morgan Kaufmann Publishers Inc .
   Google Scholar
• Dietterich , T.G. 1998 . Approximate statistical test for comparing supervised classification learning algorithms . Neural Computation , 10 ( 7 ) : 1895 – 1923 .
   PubMed Web of Science ®Google Scholar
• Knowles , J. and Corne , D. 2002 . “ On metrics for comparing nondominated sets ” . In In 2002 Congress on Evolutionary Computation CEC 02 Vol. 1 , 711 – 716 .
   Google Scholar