Fuzzy Gaussian Process Classification Model

Eman Ahmed*, Neamat El Gayar, Amir F. Atiya, Iman A. El-Azab

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Soft labels allow a pattern to belong to multiple classes with different degrees. In many real world applications the association of a pattern to multiple classes is more realistic; to describe overlap and uncertainties in class belongingness. The objective of this work is to develop a fuzzy Gaussian process model for classification of soft labeled data. Gaussian process models have gained popularity in the recent years in classification and regression problems and are example of a flexible, probabilistic, non-parametric model with uncertainty predictions. Here we derive a fuzzy Gaussian model for a two class problem and then explain how this can be extended to multiple classes. The derived model is tested on different fuzzified datasets to show that it can adopt to various classification problems. Results reveal that our model outperforms the fuzzy K-Nearest Neighbor (FKNN), applied on the fuzzified dataset, as well as the Gaussian process and the K-Nearest Neighbor models used with crisp labels.

Original languageEnglish
Title of host publicationImage Analysis and Recognition. ICIAR 2009
Number of pages8
ISBN (Electronic)9783642026119
ISBN (Print)9783642026102
Publication statusPublished - 2009
Event6th International Conference on Image Analysis and Recognition 2009 - Halifax, Canada
Duration: 6 Jul 20098 Jul 2009

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Conference on Image Analysis and Recognition 2009
Abbreviated titleICIAR 2009


  • Fuzzy Classification
  • Gaussian Process(es)
  • Soft labels

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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