Fuzzy classification systems is defined in this paper as an aggregative model, in such a way that Ruspini classical definition of fuzzy partition appears as a particular case. Once a basic recursive model has been accepted, we then propose to analyze relevance and redundancy in order to allow the possibility of learning from previous experiences. All these concepts are applied to a real picture, showing that our approach allows to check quality of such a classification system.
@article{urn:eudml:doc:39222,
title = {Relevance and redundancy in fuzzy classification systems.},
journal = {Mathware and Soft Computing},
volume = {8},
year = {2001},
pages = {203-216},
zbl = {0998.68153},
mrnumber = {MR1902142},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39222}
}
Del Amo, Ana; Gómez, Daniel; Montero, Javier; Biging, Gregory S. Relevance and redundancy in fuzzy classification systems.. Mathware and Soft Computing, Tome 8 (2001) pp. 203-216. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39222/