Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)

A Genetic Tuned Fuzzy Classifier Based on Prototypes

Authors
Enrique Leyva, Antonio González, Raúl Pérez
Corresponding Author
Enrique Leyva
Available Online August 2013.
DOI
https://doi.org/10.2991/eusflat.2013.14How to use a DOI?
Keywords
Fuzzy Classifiers Evolutionary Algorithms Nearest Neighbor Instance Selection
Abstract
It is known that main drawbacks of KNN classifier are related to the need for keeping all the training prototypes. Although there are several approaches capable to significantly reduce the size of the case base, they damage the classification accuracy. We propose a novel fuzzy approach capable to significantly reduce the prototypes base while improving the classification accuracies of KNN. It includes an optional tuning phase to be performed by an evolutionary algorithm, capable to further improve the accuracy of the classifier. An experimental study involving the proposal and 17 prototype based clas-sifiers on 30 databases validates the proposal.
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Proceedings
8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90786-77-78-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/eusflat.2013.14How 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  - Enrique Leyva
AU  - Antonio González
AU  - Raúl Pérez
PY  - 2013/08
DA  - 2013/08
TI  - A Genetic Tuned Fuzzy Classifier Based on Prototypes
BT  - 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.14
DO  - https://doi.org/10.2991/eusflat.2013.14
ID  - Leyva2013/08
ER  -