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Article Dans Une Revue International Journal of Computational Intelligence Année : 2008

The Labeled Classification and Its Application

Mohamed Nemissi
  • Fonction : Auteur
Hamid Seridi
  • Fonction : Auteur
Herman Akdag
  • Fonction : Auteur
  • PersonId : 903641

Résumé

This paper presents and evaluates a new classification method that aims to improve classifiers performances and speed up their training process. The proposed approach, called labeled classification, seeks to improve convergence of the BP (Back propagation) algorithm through the addition of an extra feature (labels) to all training examples. To classify every new example, tests will be carried out each label. The simplicity of implementation is the main advantage of this approach because no modifications are required in the training algorithms. Therefore, it can be used with others techniques of acceleration and stabilization. In this work, two models of the labeled classification are proposed: the LMLP (Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro Fuzzy Classifier). These models are tested using Iris, wine, texture and human thigh databases to evaluate their performances.
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Dates et versions

hal-01336163 , version 1 (22-06-2016)

Identifiants

  • HAL Id : hal-01336163 , version 1

Citer

Mohamed Nemissi, Hamid Seridi, Herman Akdag. The Labeled Classification and Its Application. International Journal of Computational Intelligence, 2008, 2 (6), pp.2088-2097. ⟨hal-01336163⟩
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