Pattern classification through fuzzy likelihood

Authors

  • Rosa M. Pidatella
  • Giovanni Gallo
  • Masoumeh Zeinali

Keywords:

Fuzzy likelihood, Pattern recognition

Abstract

This paper introduces a novel way to compute the membership function of a fuzzy set approximating the distribution of some observed data starting with their histogram. This membership function is in turn used to obtain a posteriori probability through a suitable version of the Bayesian formula. The ordering imposed by an  overtaking relation between fuzzy numbers translates immediately into a dominance of the a posteriori probability of a class over another for a given observed value. In this way a crisp classification is eventually obtained.

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Published

2015-11-05

Issue

Vol. 70 No. 2 (2015)

Section

Articoli

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