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On the suitability of Extreme Learning Machine for gene classification using feature selection., , , , , and . ISDA, page 507-512. IEEE, (2010)Learning Artificial Neural Networks multiclassifiers by evolutionary multiobjective differential evolution guided by statistical distributions., , , and . IJCNN, page 1-8. IEEE, (2010)Tackling the ordinal and imbalance nature of a melanoma image classification problem., , , , and . IJCNN, page 2156-2163. IEEE, (2016)On the use of evolutionary time series analysis for segmenting paleoclimate data., , , , , , and . Neurocomputing, (2019)Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images., , , and . IEEE Trans. Med. Imaging, 35 (4): 1036-1045 (2016)Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology., , , , and . Evolutionary Intelligence, 3 (3-4): 187-199 (2010)MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networks., , , and . Neurocomputing, 74 (16): 2502-2510 (2011)Numerical variable reconstruction from ordinal categories based on probability distributions., , , , , and . ISDA, page 1182-1187. IEEE, (2011)Ordinal Regression Methods: Survey and Experimental Study., , , , and . IEEE Trans. Knowl. Data Eng., 28 (1): 127-146 (2016)Tackling the ordinal and imbalance nature of a melanoma image classification problem., , , , and . IJCNN, page 2156-2163. IEEE, (2016)