The LNFC: labeled neuro-fuzzy classifier

Authors:
M. Nemissi;H. Seridi;H. Akdag
Affiliations:
LAIG, Université de Guelma, Guelma, Algérie;LAIG, Université de Guelma, Guelma, Algérie and CReSTIC, LERI, Université de Reims Champagne Ardenne, France;CReSTIC, LERI, Université de Reims Champagne Ardenne, France and LIP6, Université P. & M. Curie, Paris, France
Venue:
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Year:
2005

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Abstract

This paper presents a model of Neuro-Fuzzy classification, which its conception is inspired from the labeled classification using Neural Networks. This last aims to improve the classification performances and to accelerate the training of the used classifier. It is based on the addition of a set of labels to all training examples. Tests will be then carried out with each of these labels to classify a new example. The advantage of this approach is the simplicity of its implementation, which does not require modification of the training algorithm. The proposed model is based on the use of this method with the NFC (Neuro Fuzzy Classifier). To appreciate its performances, tests are carried out on the Iris and human thigh data basis by the NFC with and without labels.