RT Book, Section
T1 Determining the accuracy in supervised fuzzy classification problems
A1 Gómez, Daniel
A1 Montero, Javier
A2 Ruan, Da
A2 Montero, Javier
AB A large number of accuracy measures for image classification are actually available in the literature for cris classification. Overall accuracy, producer accuracy, user accuracy, kappa index and tau value are some examples. But in contrast to this effort in measuring the accuracy in a crisp framework, few proposals can be found in order to determine accuracy for soft classifiers. In this paper we define some accuracy measures for soft classification that extend some classical accuracy measures for crisp classifiers. This elms of measures takes into account the preferences of the decision maker in order to differentiate some errors that in practice may not be have same relevance.
PB World Scientific
SN 978-981-279-946-3
YR 2008
FD 2008-09-21
LK https://hdl.handle.net/20.500.14352/53161
UL https://hdl.handle.net/20.500.14352/53161
LA eng
NO 8th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science. SEP 21-24, 2008
DS Docta Complutense
RD 16 may 2024