The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The objective of this work is to select artificial neural network models (ANN) automatically with sigmoid basis units for multiclassification tasks. These models are designed using a Memetic Pareto Differential Evolution Neural Network algorithm (MPDENN) based on the Pareto dominance concept. We propose different methodologies to obtain the best model from the Pareto front obtained with the MPDENN...
The selection of a particular neural network model belonging to the Pareto front is a problem that exists in all multi-objective algorithms. This paper proposes a novel solution to this problem based on a linear combination of the outputs of the two extremes in the Pareto front, which form an ensemble. The decision support TOPSIS method is used to determine which linear combination creates the best...
This paper studies the suitability of Extreme Learning Machines (ELM) for resolving bioinformatic and biomedical classification problems. In order to test their overall performance, an experimental study is presented based on five gene microarray datasets found in bioinformatic and biomedical domains. The Fast Correlation-Based Filter (FCBF) was applied in order to identify salient expression genes...
In this paper, we propose a memetic algorithm (MA) for classifier optimization based on a clustering method that applies the k-means algorithm over a specific derived space. In this space, each classifier or individual is represented by the set of the accuracies of the classifier for each class of the problem. The proposed sensitivity clustering is able to obtain groups of individuals that perform...
This paper proposes a novelty neural network model by using generalized kernel functions for the hidden layer of a feed forward network (Generalized Radial Basis Functions, GRBF), where the architecture, weights and node typology are learned through an evolutionary programming algorithm. This new kind of model is compared with the corresponding models with standard hidden nodes: Product Unit Neural...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.