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Fernandez-Navarro F.,University of Cordoba, Spain | Hervas-Martinez C.,University of Cordoba, Spain | Gutierrez P.A.,University of Cordoba, Spain | Carbonero-Ruz M.,ETEA
Neural Networks | Year: 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 taken from the UCI repository is presented. The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q-Gaussian model can be considered very competitive with the other classification methods. © 2011 Elsevier Ltd.


Fernandez J.C.,University of Cordoba, Spain | Hervas C.,University of Cordoba, Spain | Martinez-Estudillo F.J.,ETEA | Gutierrez P.A.,University of Cordoba, Spain
Applied Soft Computing Journal | Year: 2011

The main objective of this work is to automatically design neural network models with sigmoid basis units for binary classification tasks. The classifiers that are obtained achieve a double objective: a high classification level in the dataset and a high classification level for each class. We present MPENSGA2, a Memetic Pareto Evolutionary approach based on the NSGA2 multiobjective evolutionary algorithm which has been adapted to design Artificial Neural Network models, where the NSGA2 algorithm is augmented with a local search that uses the improved Resilient Backpropagation with backtracking - IRprop+ algorithm. To analyze the robustness of this methodology, it was applied to four complex classification problems in predictive microbiology to describe the growth/no-growth interface of food-borne microorganisms such as Listeria monocytogenes, Escherichia coli R31, Staphylococcus aureus and Shigella flexneri. The results obtained in Correct Classification Rate (CCR), Sensitivity (S) as the minimum of sensitivities for each class, Area Under the receiver operating characteristic Curve (AUC), and Root Mean Squared Error (RMSE), show that the generalization ability and the classification rate in each class can be more efficiently improved within a multiobjective framework than within a single-objective framework. © 2010 Elsevier B.V. All rights reserved.


Fernandez Caballero J.C.,University of Cordoba, Spain | Martinez F.J.,ETEA | Hervas C.,University of Cordoba, Spain | Gutierrez P.A.,University of Cordoba, Spain
IEEE Transactions on Neural Networks | Year: 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 need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class. © 2010 IEEE.


Fernandez-Navarro F.,University of Cordoba, Spain | Hervas-Martinez C.,University of Cordoba, Spain | Garcia-Alonso C.,ETEA | Torres-Jimenez M.,ETEA
Expert Systems with Applications | Year: 2011

In this paper, a dynamic over-sampling procedure is proposed to improve the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a Hybrid algorithm (HA) that optimizes Multi Layer Perceptron Neural Networks (MLPs). To handle class imbalance, the training dataset is resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to partially balance the size of the classes. In the second, the HA is run and the dataset is over-sampled in different generations of the evolution, generating new patterns in the minimum sensitivity class (the class with the worst accuracy for the best MLP of the population). To evaluate the efficiency of our technique, we pose a complex problem, the classification of 1617 real farms into three classes (efficient, intermediate and inefficient) according to the Relative Technical Efficiency (RTE) obtained by the Monte Carlo Data Envelopment Analysis (MC-DEA). The multi-classification model, named Dynamic Smote Hybrid Multi Layer Perceptron (DSHMLP) is compared to other standard classification methods with an over-sampling procedure in the preprocessing stage and to the threshold-moving method where the output threshold is moved toward inexpensive classes. The results show that our proposal is able to improve minimum sensitivity in the generalization set (35.00%) and obtains a high accuracy level (72.63%). © 2010 Elsevier Ltd. All rights reserved.


Gutierrez P.A.,University of Cordoba, Spain | Hervas-Martinez C.,University of Cordoba, Spain | Martinez-Estudillo F.J.,ETEA | Carbonero M.,ETEA
Information Sciences | Year: 2012

The machine learning community has traditionally used correct classification rates or accuracy (C) values to measure classifier performance and has generally avoided presenting classification levels of each class in the results, especially for problems with more than two classes. C values alone are insufficient because they cannot capture the myriad of contributing factors that differentiate the performance of two different classifiers. Receiver Operating Characteristic (ROC) analysis is an alternative to solve these difficulties, but it can only be used for two-class problems. For this reason, this paper proposes a new approach for analysing classifiers based on two measures: C and sensitivity (S) (i.e., the minimum of accuracies obtained for each class). These measures are optimised through a two-stage evolutionary process. It was conducted by applying two sequential fitness functions in the evolutionary process, including entropy (E) for the first stage and a new fitness function, area (A), for the second stage. By using these fitness functions, the C level was optimised in the first stage, and the S value of the classifier was generally improved without significantly reducing C in the second stage. This two-stage approach improved S values in the generalisation set (whereas an evolutionary algorithm (EA) based only on the S measure obtains worse S levels) and obtained both high C values and good classification levels for each class. The methodology was applied to solve 16 benchmark classification problems and two complex real-world problems in analytical chemistry and predictive microbiology. It obtained promising results when compared to other competitive multi-class classification algorithms and a multi-objective alternative based on E and S. © 2012 Elsevier Inc. All rights reserved.


Sanchez-Monedero J.,University of Cordoba, Spain | Hervas-Martinez C.,University of Cordoba, Spain | Gutierrez P.A.,University of Cordoba, Spain | Ruz M.C.,ETEA | And 2 more authors.
Neural Network World | Year: 2010

Accuracy alone can be deceptive when evaluating the performance of a classifier, especially if the problem involves a high number of classes. This paper proposes an approach used for dealing with multi-class problems, which tries to avoid this issue. The approach is based on the Extreme Learning Machine (ELM) classifier, which is trained by using a Differential Evolution (DE) algorithm. Two error measures (Accuracy, C, and Sensitivity, S) are combined and applied as a fitness function for the algorithm. The proposed approach is able to obtain multi-class classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is evaluated over seven benchmark classification problems and one real problem, obtaining promising results. © ICS AS CR 2010.


Garcia-Alonso C.R.,ETEA | Arenas-Arroyo E.,Charles III University of Madrid | Perez-Alcala G.M.,ETEA
Expert Systems with Applications | Year: 2012

A computer system based on Monte-Carlo simulation and fuzzy logic has been designed, developed and tested to: (i) identify covariates that influence remittances received in a specific country and (ii) explain their behavior throughout the time span involved. The resulting remittance model was designed theoretically, identifying the variables which determined remittances and their dependence relationships, and then developed into a computer cluster. This model aims to be global and is useful for assessing the long term evolution of remittances in scenarios where a rich country is the host (United States of America) while a poor country is the where the migrant is from (El Salvador). By changing the socio-economic characteristics of the countries involved, experts can analyze new socio-economic frameworks to obtain useful conclusions for decision-making processes involving development and sustainability. © 2012 Elsevier Ltd. All rights reserved.


Sanchez-Monedero J.,University of Cordoba, Spain | Carbonero-Ruz M.,ETEA | Becerra-Alonso D.,ETEA | Martinez-Estudillo F.J.,ETEA | And 2 more authors.
International Conference on Intelligent Systems Design and Applications, ISDA | Year: 2011

Ordinal classification problems are an active research area in the machine learning community. Many previous works adapted state-of-art nominal classifiers to improve ordinal classification so that the method can take advantage of the ordinal structure of the dataset. However, these method improvements often rely upon a complex mathematical basis and they usually are attached to the training algorithm and model. This paper presents a novel method for generally adapting classification and regression models, such as artificial neural networks or support vector machines. The ordinal classification problem is reformulated as a regression problem by the reconstruction of a numerical variable which represents the different ordered class labels. Despite the simplicity and generality of the method, results are competitive in comparison with very specific methods for ordinal regression. © 2011 IEEE.


Fernandez-Navarro F.,University of Cordoba, Spain | Hervas-Martinez C.,University of Cordoba, Spain | Gutierrez P.A.,University of Cordoba, Spain | Cruz-Ramirez M.,University of Cordoba, Spain | Carbonero-Ruz M.,ETEA
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop∈+ algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with eleven datasets, taken from the UCI repository is presented. The RBFNN with the q-Gaussian is compared to RBFNN with Gaussian, Cauchy and Inverse Multiquadratic RBFs. © 2010 Springer-Verlag.


Sanchez-Monedero J.,University of Cordoba, Spain | Hervas-Martinez C.,University of Cordoba, Spain | Martinez-Estudillo F.J.,ETEA | Ruz M.C.,ETEA | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Accuracy alone is insufficient to evaluate the performance of a classifier especially when the number of classes increases. This paper proposes an approach to deal with multi-class problems based on Accuracy (C) and Sensitivity (S). We use the differential evolution algorithm and the ELM-algorithm (Extreme Learning Machine) to obtain multi-classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is applied to solve four benchmark classification problems and obtains promising results. © 2010 Springer-Verlag.

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