Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)

Determining the accuracy in image supervised classification problems

Authors
Daniel Gomez, Javier Montero
Corresponding Author
Daniel Gomez
Available Online August 2011.
DOI
https://doi.org/10.2991/eusflat.2011.103How to use a DOI?
Keywords
Fuzzy image classification, Accuracy measures; Kappa Index.
Abstract
A large number of accuracy measures for crisp supervised classification have been developed in supervised image classification literature. Overall accuracy, Kappa index, Kappa location, Kappa histo and user accuracy are some well-known examples. In this work, we will extend and analyze some of these measures in a fuzzy framework to be able to measure the goodness of a given classifier in a supervised fuzzy classification system with fuzzy reference data. In addition with this, the measures here defined also take into account the preferences of the decision maker in order to differentiate some errors that must not be considered equal in the classification process.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2011
ISBN
978-90-78677-00-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/eusflat.2011.103How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Daniel Gomez
AU  - Javier Montero
PY  - 2011/08
DA  - 2011/08
TI  - Determining the accuracy in image supervised classification problems
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 342
EP  - 349
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.103
DO  - https://doi.org/10.2991/eusflat.2011.103
ID  - Gomez2011/08
ER  -