Determining the accuracy in image supervised classification problems

DOI

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.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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 (EUSFLAT-11)
PB  - Atlantis Press
SP  - 342
EP  - 349
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.103
DO  - 10.2991/eusflat.2011.103
ID  - Gomez2011/08
ER  -