Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes

@article{Muhlbaier2009LearnC,
  title={Learn\$^\{++\}\$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes},
  author={Michael Muhlbaier and Apostolos Topalis and Robi Polikar},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={152-168}
}
We have previously introduced an incremental learning algorithm Learn++, which learns novel information from consecutive data sets by generating an ensemble of classifiers with each data set, and combining them by weighted majority voting. However, Learn++ suffers from an inherent ldquooutvotingrdquo problem when asked to learn a new class omeganew introduced by a subsequent data set, as earlier classifiers not trained on this class are guaranteed to misclassify omeganew instances. The… CONTINUE READING
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Additional simulation results on Learn NC

  • R. Polikar, M. Muhlbaier
  • Jul. 18, 2008 [Online]. Available: http://www…
  • 2008
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