Heterogeneous classifier ensemble with fuzzy rule-based meta learner

Abonyi, 2003, Data-driven generation of compact, accurate, and linguistically-sound fuzzy classifiers based on a decision-tree initialization, Int. J. Approx. Reasoning, 32, 1, 10.1016/S0888-613X(02)00076-2 Alcalá-Fdez, 2009, Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms, Fuzzy Sets Syst., 160, 905, 10.1016/j.fss.2008.05.012 Angelov, 2008, Evolving fuzzy-rule-based classifiers from data streams, IEEE Trans. Fuzzy Syst., 16, 1462, 10.1109/TFUZZ.2008.925904 Bishop, 2006 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Breiman, 2001, Random forest, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Casillas, 2005, Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction, IEEE Trans. Fuzzy Syst., 13, 13, 10.1109/TFUZZ.2004.839670 Demsar, 2006, Statistical comparisons of classifiers over multiple datasets, J. Mach. Learn. Res., 7, 1 Fan, 2008, LIBLINEAR: a library for large linear classification, J. Mach. Learn. Res., 9, 1871 Freund, 1996, Experiments with a new boosting algorithm, 148 Garcia, 2008, An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons, J. Mach. Learn. Res., 9, 2579 Gonzáles, 1999, SLAVE: a genetic learning system based on an iterative approach, IEEE Trans. Fuzzy Syst., 7, 176, 10.1109/91.755399 Hong, 2006, A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions, Soft Comput., 10, 1091, 10.1007/s00500-006-0046-x Hong, 2000, Learning a coverage set of maximally general fuzzy rules by rough sets, Expert Syst. Appl., 19, 97, 10.1016/S0957-4174(00)00024-5 Ishibuchi, 1999, Voting in fuzzy rules-based systems for pattern classification problems, Fuzzy Sets Syst., 103, 223, 10.1016/S0165-0114(98)00223-1 Ishibuchi, 2001, Three-objective genetics-based machine learning for linguistic rule extraction, Inf. Sci., 136, 109, 10.1016/S0020-0255(01)00144-X Ishibuchi, 2007, Analysis of interpretability-accuracy tradeoff of fuzzy systems by multi-objective fuzzy genetics-based machine learning, Int. J. Approx. Reasoning, 44, 4, 10.1016/j.ijar.2006.01.004 Ishibuchi, 2004, Comparison of heuristic criteria for fuzzy rule selection in classification problems, Fuzzy Optim. Decis. Making, 3, 119, 10.1023/B:FODM.0000022041.98349.12 Ishibuchi, 2005, Hybridization of fuzzy GBML approaches for pattern classifications problems, IEEE Trans. Syst. Man Cybern., 35, 359, 10.1109/TSMCB.2004.842257 Jahromi, 2008, A proposed method for learning rule weights in fuzzy rule-based classification systems, Fuzzy Sets Syst., 159, 449, 10.1016/j.fss.2007.08.007 Kim, 2005, Optimized fuzzy classification using genetic algorithm, 392 Kittler, 1998, On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell., 20, 226, 10.1109/34.667881 Kuncheva, 2001, Decision templates for multi classifier fusion: an experimental comparison, Pattern Recognit., 34, 299, 10.1016/S0031-3203(99)00223-X Mansoori, 2007, A weighting function for improving fuzzy classification systems performance, Fuzzy Sets Syst., 158, 583, 10.1016/j.fss.2006.10.004 Mansoori, 2008, SGERD: a steady-state genetic algorithm for extracting fuzzy classification rules from data, IEEE Trans. Fuzzy Syst., 16, 1061, 10.1109/TFUZZ.2008.915790 Merz, 1999, Using correspondence analysis to combine classifiers, Mach. Learn., 36, 33, 10.1023/A:1007559205422 Mitra, 2000, Neural fuzzy rule generation: survey in soft computing framework, IEEE Trans. Neural Netw., 11, 748, 10.1109/72.846746 Nguyen, 2014, Optimization of ensemble classifier system based on multiple objectives genetic algorithm, 46 Nguyen, 2014, Combining multi classifiers based on a genetic algorithm – a gaussian mixture model framework, 56 Nguyen, 2014, A novel genetic algorithm approach for simultaneous feature and classifier selection in multi classifier system, 1698 Nguyen, 2016, A novel combining classifier method based on variational inference, Pattern Recognit., 49, 198, 10.1016/j.patcog.2015.06.016 Roubos, 2003, Learning fuzzy classification rules for labeled data, Inf. Sci., 150, 77, 10.1016/S0020-0255(02)00369-9 Sen, 2013, Linear classifier combination and selection using group sparse regularization and hinge loss, Pattern Recognit. Lett., 34, 265, 10.1016/j.patrec.2012.10.008 Sesmero, 2015, Generating ensembles of heterogeneous classifiers using stacked generalization, Wiley Interdiscip. Rev., 5, 21 Soua, 2013, An ensemble method for fuzzy rule-based classification systems, Knowl. Inf. Syst., 36, 385, 10.1007/s10115-012-0532-7 Su, 2006, A Fast Decision Tree Learning Algorithm, 1, 500 Ting, 1999, Issues in stacked generation, J. Artif. Intell. Res., 10, 271, 10.1613/jair.594 Todorovski, 2003, Combining classifiers with meta decision trees, Mach. Learn., 50, 223, 10.1023/A:1021709817809 UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html. Verikas, 2011, A general framework for designing a fuzzy rule-based classifier, Knowl. Inf. Syst., 29, 203, 10.1007/s10115-010-0340-x Wang, 2009, Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy, IEEE Trans. Fuzzy Syst., 17, 556, 10.1109/TFUZZ.2008.924342 Wang, 2001, A comparative study on heuristic algorithm for generating fuzzy decision trees, IEEE Trans. Syst. Man Cybern Part B., 31, 215, 10.1109/3477.915344 Yu, 2003, Feature selection for high-dimensional data: a fast correlation-based filter solution, 856 Zhang, 2011, An experimental study of one-and-two-level classifier fusion for different sample sizes, Pattern Recognit. Lett., 32, 1756, 10.1016/j.patrec.2011.07.009 Zhang, 2011, Sparse ensembles using weighted combination methods based on linear programming, Pattern Recognit., 44, 97, 10.1016/j.patcog.2010.07.021 Zhou, 2012