Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy

@article{Krawczyk2016EvolutionaryUB,
  title={Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy},
  author={B. Krawczyk and M. Galar and L. Jelen and F. Herrera},
  journal={Appl. Soft Comput.},
  year={2016},
  volume={38},
  pages={714-726}
}
Graphical abstractDisplay Omitted HighlightsAutomatic clinical decision support system for breast cancer malignancy grading.Different methodologies for segmentation and feature extraction from FNA slides.An efficient classifier ensemble for imbalanced problems with difficult data.Ensemble combines boosting with evolutionary undersampling.Extensive computational experiments on a large database collected by authors. In this paper, we propose a complete, fully automatic and efficient clinical… Expand
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