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Learning similarity metric to improve the performance of lazy multi-label ranking algorithms., , and . ISDA, page 246-251. IEEE, (2012)Effective active learning strategy for multi-label learning., , and . Neurocomputing, (2018)Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context., , and . Neurocomputing, (2015)A gene expression programming method for multi-target regression., , , and . LOPAL, page 2:1-2:6. ACM, (2018)A locally weighted learning method based on a data gravitation model for multi-target regression., , , and . Int. J. Comput. Intell. Syst., 11 (1): 282-295 (2018)ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning., , and . CIARP (2), volume 8259 of Lecture Notes in Computer Science, page 528-535. Springer, (2013)A Supervised Methodology for Analyzing Dysregulation in Splicing Machinery: An Application in Cancer Diagnosis., , , and . CBMS, page 120-125. IEEE, (2019)Evolutionary feature weighting to improve the performance of multi-label lazy algorithms., , and . Integr. Comput. Aided Eng., 21 (4): 339-354 (2014)JCLAL: A Java Framework for Active Learning., , , , and . J. Mach. Learn. Res., (2016)An ensemble-based method for the selection of instances in the multi-target regression problem., , and . Integr. Comput. Aided Eng., 25 (4): 305-320 (2018)