Lazy Bagging for Classifying Imbalanced Data

  • Xingquan Zhu
  • Published 2007 in
    Seventh IEEE International Conference on Data…

Abstract

In this paper, we propose a lazy bagging (LB) design, which builds bootstrap replicate bags based on the characteristics of the test instances. Upon receiving a test instance Ik, LB will trim bootstrap bags by taking Ik's nearest neighbors in the training set into consideration. Our hypothesis is that an unlabeled instance's nearest neighbors provide… (More)
DOI: 10.1109/ICDM.2007.95

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