A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets

@article{Fernndez2017APE,
  title={A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets},
  author={Alberto Fern{\'a}ndez and Crist{\'o}bal Jos{\'e} Carmona and Mar{\'i}a Jos{\'e} del Jes{\'u}s and Francisco Herrera},
  journal={International journal of neural systems},
  year={2017},
  volume={27 6},
  pages={
          1750028
        }
}
Imbalanced classification is related to those problems that have an uneven distribution among classes. [] Key Method Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples.

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