Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability

  • Roberto D’Ambrosio
  • Wafa Bel Haj Ali
  • Richard Nock
  • Paolo Soda
  • Frank Nielsen
  • Michel Barlaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7588)

Abstract

Universal Nearest Neighbours (unn) is a classifier recently proposed, which can also effectively estimates the posterior probability of each classification act. This algorithm, intrinsically binary, requires the use of a decomposition method to cope with multiclass problems, thus reducing their complexity in less complex binary subtasks. Then, a reconstruction rule provides the final classification. In this paper we show that the application of unn algorithm in conjunction with a reconstruction rule based on the posterior probabilities provides a classification scheme robust among different biomedical image datasets. To this aim, we compare unn performance with those achieved by Support Vector Machine with two different kernels and by a k Nearest Neighbours classifier, and applying two different reconstruction rules for each of the aforementioned classification paradigms. The results on one private and five public biomedical datasets show satisfactory performance.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Roberto D’Ambrosio
    • 1
    • 3
  • Wafa Bel Haj Ali
    • 3
  • Richard Nock
    • 2
  • Paolo Soda
    • 1
  • Frank Nielsen
    • 4
  • Michel Barlaud
    • 3
    • 5
  1. 1.Universita’ Campus Bio-Medico di RomaRomeItaly
  2. 2.CEREGMIA - Université Antilles-GuyaneMartiniqueFrance
  3. 3.CNRS - U.NiceFrance
  4. 4.Sony Computer Science Laboratories, Inc.TokyoJapan
  5. 5.Institut Universitaire deFrance

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