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Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning

  • Carlos MeraEmail author
  • Mauricio Orozco-Alzate
  • John Branch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

Abstract

In standard supervised learning, the problem of learning from imbalanced data has been addressed to improve the performance of learning algorithms in the presence of underrepresented data. However, in Multiple-Instance Learning (MIL), where the imbalance problem is more complex, there is little discussion about it. Motivated by the need of further studies, we discuss the multiple-instance imbalance problem and propose a method to improve the representation of the positive class. Our approach looks for the target concept in positive bags and tries to strength it using an oversampling technique while removes the borderline (ambiguous) instances in positive and negative bags. We evaluate our method on several standard MIL benchmarking data sets in order to show its ability to get an enhanced representation of the positive class.

Keywords

Multiple-instance learning Class imbalance learning Oversampling Undersampling 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Carlos Mera
    • 1
    Email author
  • Mauricio Orozco-Alzate
    • 2
  • John Branch
    • 1
  1. 1.Universidad Nacional de Colombia, Sede MedellínMedellínColombia
  2. 2.Universidad Nacional de Colombia, Sede ManizalesManizalesColombia

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