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Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains

  • Evgeni Tsivtsivadze
  • Tom Heskes
  • Armand Paauw
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)

Abstract

In various learning problems data can be available in different representations, often referred to as views. We propose multi-class classification method that is particularly suitable for multi-view learning setting. The algorithm uses co-regularization and error-correcting techniques to leverage information from multiple views and in our empirical evaluation notably outperforms several state-of-the-art classification methods on publicly available datasets. Furthermore, we apply the proposed algorithm for identification of the pathogenic bacterial strains from the recently collected biomedical dataset. Our algorithm gives a low classification error rate of 5%, allows rapid identification of the pathogenic microorganisms, and can aid effective response to an infectious disease outbreak.

Keywords

Reproduce Kernel Hilbert Space Multiple Kernel Learning Machine Learn Research Brucella Species Infectious Disease Outbreak 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Evgeni Tsivtsivadze
    • 1
  • Tom Heskes
    • 2
  • Armand Paauw
    • 1
  1. 1.MSB GroupThe Netherlands Organization for Applied Scientific ResearchZeistThe Netherlands
  2. 2.Institute for Computing and Information SciencesRadboud UniversityThe Netherlands

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