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Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure., , and . Neurocomputing, (2016)Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness., , , and . Inf. Sci., (2013)SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering., , , and . Inf. Sci., (2015)Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation., , , , , and . Expert Syst. Appl., (2019)A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees., , , and . HAIS (2), volume 7209 of Lecture Notes in Computer Science, page 25-35. Springer, (2012)Shared domains of competence of approximate learning models using measures of separability of classes., and . Inf. Sci., 185 (1): 43-65 (2012)DILS: Constrained clustering through dual iterative local search., , , and . Comput. Oper. Res., (2020)A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training., , and . CoRR, (2021)Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method., and . Fuzzy Sets Syst., 161 (1): 3-19 (2010)A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost., , and . HAIS, volume 10334 of Lecture Notes in Computer Science, page 268-280. Springer, (2017)