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A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets
2017
International Journal of Neural Systems
Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selection.
doi:10.1142/s0129065717500289
pmid:28633551
fatcat:3avzppodxrgptmymycxcwlzkqa