Relative Minimum Distance Between Projected Bags for Improved Multiple Instance Classification

  • José Francisco Ruiz-Muñoz
  • Germán Castellanos-Dominguez
  • Mauricio Orozco-AlzateEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)


A novel relative minimum distance is introduced that allows improving the dissimilarity-based multiple instance classification. To this end, we apply a previously proposed mapping that brings closer, at least, a single instance from each positive training bag, while the negative-bags instances are driven apart. Our results show an increased classification performance on a broad type of real-world datasets.


Multiple Instance Learning Metric Learning Nearest-neighbor rule 



This work is partially supported by “Convocatoria 567 de 2012 - Colciencias”. The authors acknowledge support to attend SISAP 2018 provided by “Convocatoria para la Movilidad Internacional de la Universidad Nacional de Colombia (UNAL) 2017–2018”.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • José Francisco Ruiz-Muñoz
    • 1
    • 2
  • Germán Castellanos-Dominguez
    • 1
  • Mauricio Orozco-Alzate
    • 1
    Email author
  1. 1.Universidad Nacional de Colombia - Sede ManizalesManizalesColombia
  2. 2.Instituto Tecnológico MetropolitanoMedellínColombia

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