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@ARTICLE{Collell:845280,
      author       = {Collell, Guillem and Prelec, Drazen and Patil, Kaustubh},
      title        = {{A} simple plug-in bagging ensemble based on
                      threshold-moving for classifying binary and multiclass
                      imbalanced data},
      journal      = {Neurocomputing},
      volume       = {275},
      issn         = {0925-2312},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2018-02561},
      pages        = {330 - 340},
      year         = {2018},
      abstract     = {Class imbalance presents a major hurdle in the application
                      of classification methods. A commonly taken approach is to
                      learn ensembles of classifiers using rebalanced data.
                      Examples include bootstrap averaging (bagging) combined with
                      either undersampling or oversampling of the minority class
                      examples. However, rebalancing methods entail asymmetric
                      changes to the examples of different classes, which in turn
                      can introduce their own biases. Furthermore, these methods
                      often require specifying the performance measure of interest
                      a priori, i.e., before learning. An alternative is to employ
                      the threshold moving technique, which applies a threshold to
                      the continuous output of a model, offering the possibility
                      to adapt to a performance measure a posteriori, i.e., a
                      plug-in method. Surprisingly, little attention has been paid
                      to this combination of a bagging ensemble and
                      threshold-moving. In this paper, we study this combination
                      and demonstrate its competitiveness. Contrary to the other
                      resampling methods, we preserve the natural class
                      distribution of the data resulting in well-calibrated
                      posterior probabilities. Additionally, we extend the
                      proposed method to handle multiclass data. We validated our
                      method on binary and multiclass benchmark data sets by using
                      both, decision trees and neural networks as base
                      classifiers. We perform analyses that provide insights into
                      the proposed method.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29398782},
      UT           = {WOS:000418370200032},
      doi          = {10.1016/j.neucom.2017.08.035},
      url          = {https://juser.fz-juelich.de/record/845280},
}