title: |
Determining the accuracy in image supervised classification problems |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
| pages: | 342 - 349 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Daniel G¨®mez, Javier Montero |
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publication date: |
July 2011 |
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keywords: |
Fuzzy image classification, Accuracy
measures; Kappa Index. |
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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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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |