Multi-labeler Classification Using Kernel Representations and Mixture of Classifiers

  • D. E. Imbajoa-Ruiz
  • I. D. Gustin
  • M. Bolaños-Ledezma
  • A. F. Arciniegas-Mejía
  • F. A. Guasmayan-Guasmayan
  • M. J. Bravo-Montenegro
  • A. E. Castro-Ospina
  • D. H. Peluffo-Ordóñez
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)

Abstract

This work introduces a multi-labeler kernel novel approach for data classification learning from multiple labelers. The learning process is done by training support-vector machine classifiers using the set of labelers (one labeler per classifier). The objective functions representing the boundary decision of each classifier are mixed by means of a linear combination. Followed from a variable relevance, the weighting factors are calculated regarding kernel matrices representing each labeler. To do so, a so-called supervised kernel function is also introduced, which is used to construct kernel matrices. Our multi-labeler method reaches very good results being a suitable alternative to conventional approaches.

Keywords

Multi-labeler classification Supervised kernel Support vector machines 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • D. E. Imbajoa-Ruiz
    • 1
  • I. D. Gustin
    • 1
  • M. Bolaños-Ledezma
    • 1
  • A. F. Arciniegas-Mejía
    • 1
  • F. A. Guasmayan-Guasmayan
    • 1
    • 2
  • M. J. Bravo-Montenegro
    • 2
  • A. E. Castro-Ospina
    • 3
  • D. H. Peluffo-Ordóñez
    • 1
    • 4
  1. 1.Universidad de NariñoPastoColombia
  2. 2.Universidad MarianaPastoColombia
  3. 3.Research Center of the Instituto Tecnológico MetropolitanoMedellínColombia
  4. 4.Universidad Técnica del NorteIbarraEcuador

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