A Genetic Tuned Fuzzy Classifier Based on Prototypes

DOI

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.

Copyright

© 2013, 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  - Enrique Leyva
AU  - Antonio González
AU  - Raúl Pérez
PY  - 2013/08
DA  - 2013/08
TI  - A Genetic Tuned Fuzzy Classifier Based on Prototypes
BT  - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SP  - 96
EP  - 103
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.14
DO  - 10.2991/eusflat.2013.14
ID  - Leyva2013/08
ER  -