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Boosting fuzzy rules with low quality data in multi-class problems: Open problems and challenges.

, , and . GEFS, page 28-35. IEEE, (2013)

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Boosting fuzzy rules with low quality data in multi-class problems: Open problems and challenges., , and . GEFS, page 28-35. IEEE, (2013)An Extension of the FURIA Classification Algorithm to Low Quality Data., , and . HAIS, volume 8073 of Lecture Notes in Computer Science, page 679-688. Springer, (2013)Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets., , and . Evol. Intell., 2 (1-2): 73-84 (2009)Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data., , and . Int. J. Approx. Reason., 52 (6): 841-862 (2011)Bootstrap analysis of multiple repetitions of experiments using an interval-valued multiple comparison procedure., , , and . J. Comput. Syst. Sci., 80 (1): 88-100 (2014)Mining fuzzy association rules from low-quality data., , and . Soft Comput., 16 (5): 883-901 (2012)Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers., , and . FUZZ-IEEE, page 1-8. IEEE, (2010)A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data., , and . HAIS, volume 5572 of Lecture Notes in Computer Science, page 654-661. Springer, (2009)Cost-Sensitive Learning of Fuzzy Rules for Imbalanced Classification Problems Using FURIA., , , and . International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 22 (5): 643-676 (2014)Future Performance Modeling in Athletism with Low Quality Data-based Genetic Fuzzy Systems., , and . J. Multiple Valued Log. Soft Comput., 17 (2-3): 207-228 (2011)