Parameter estimation of q-Gaussian Radial Basis Functions Neural Networks with a Hybrid Algorithm for binary classification

Authors:
Francisco Fernández-Navarro;César Hervás-Martínez;Pedro A. Gutiérrez;Jose M. Peña-Barragán;Francisca López-Granados
Affiliations:
Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein building, 3rd floor, 14074 - Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein building, 3rd floor, 14074 - Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein building, 3rd floor, 14074 - Córdoba, Spain;Institute for Sustainable Agriculture, CSIC, P.O. Box 4084, 14080 Córdoba, Spain;Institute for Sustainable Agriculture, CSIC, P.O. Box 4084, 14080 Córdoba, Spain
Venue:
Neurocomputing
Year:
2012

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Abstract

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 Neural Networks (q-Gaussian RBFNNs). This basis function includes a supplementary degree of freedom in order to adapt the model to the distribution of data. A Hybrid Algorithm (HA) is used to search for a suitable architecture for the q-Gaussian RBFNN. The use of this type of more flexible kernel could greatly improve the discriminative power of RBFNNs. In order to test performance, the RBFNN with the q-Gaussian basis functions is compared to RBFNNs with Gaussian, Cauchy and Inverse Multiquadratic RBFs, and to other recent neural networks approaches. An experimental study is presented on 11 binary-classification datasets taken from the UCI repository. Moreover, aerial imagery taken in mid-May, mid-June and mid-July was used to evaluate the potential of the methodology proposed for discriminating Ridolfia segetum patches (one of the most dominant and harmful weeds in sunflower crops) in two naturally infested fields in southern Spain.