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- Juan Carlos Fernández, Francisco José Martínez, César Hervás-Martínez, Pedro Antonio Gutiérrez
- IEEE Transactions on Neural Networks
- 2010

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the… (More)

- Francisco Fernández-Navarro, César Hervás-Martínez, Pedro Antonio Gutiérrez
- Pattern Recognition
- 2011

Sensitivity Accuracy Memetic algorithm Imbalanced datasets Over-sampling method SMOTE a b s t r a c t Classification with imbalanced datasets supposes a new challenge for researches in the framework of machine learning. This problem appears when the number of patterns that represents one of the classes of the dataset (usually the concept of interest) is… (More)

- Pedro Antonio Gutiérrez, César Hervás-Martínez, Mariano Carbonero-Ruz, Juan Carlos Fernández
- Neurocomputing
- 2008

A classification problem is a decision-making task that many researchers have studied. A number of techniques have been proposed to perform binary classification. Neural networks are one of the artificial intelligence techniques that has had the most successful results when applied to this problem. Our proposal is the use of q-Gaussian Radial Basis Function… (More)

- Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, Peter Tiño, César Hervás-Martínez
- Neural Computation
- 2013

Ordinal classification refers to classification problems in which the classes have a natural order imposed on them because of the nature of the concept studied. Some ordinal classification approaches perform a projection from the input space to one-dimensional (latent) space that is partitioned into a sequence of intervals (one for each class). Class… (More)

- Pedro Antonio Gutierrez, Maria Perez-Ortiz, Javier Sanchez-Monedero, Francisco Fernandez-Navarro, Cesar Hervas-Martinez
- IEEE Transactions on Knowledge and Data…
- 2016

Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although… (More)

In this paper we propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks. Product-units are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to… (More)

- Francisco Fernández-Navarro, César Hervás-Martínez, Javier Sánchez-Monedero, Pedro Antonio Gutiérrez
- Neurocomputing
- 2011

In this paper, we propose a methodology for training a new model of artificial neural network called the generalized radial basis function (GRBF) neural network. This model is based on generalized Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter t. The generalized radial basis function allows different radial… (More)

- Francisco Fernández-Navarro, César Hervás-Martínez, Pedro Antonio Gutiérrez, Mariano Carbonero-Ruz
- Neural Networks
- 2011

This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets… (More)