http://hdl.handle.net/10016/4464 Wed, 22 May 2013 09:49:13 GMT 2013-05-22T09:49:13Z http://hdl.handle.net/10016/16040 Title: Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms Author(s): Segura, Carlos; Cervantes, Alejandro; Nebro, Antonio J.; Jaraíz-Simón, María Dolores; Segredo, Eduardo; García-Rodríguez, Sandra; Luna, Francisco; Gómez-Pulido, Juan A.; Miranda, Gara; Luque, Cristóbal; Alba, Enrique; Vega-Rodríguez, Miguel A.; León, Coromoto; Galván, Inés M. Abstract: This work presents the application of a parallel coopera- tive optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation im- plies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet sce- nario. The cooperation of a team of multi-objective evolutionary al- gorithms has been performed with a novel optimization model. Such model is a hybrid parallel algorithm that combines a parallel island- based scheme with a hyperheuristic approach. Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational resources to the most suitable algorithms. The obtained results for a manets scenario, representing a mall, demon- strate the validity of the new proposed approach. Description: Proceeding of: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009 Wed, 31 Dec 2008 23:00:00 GMT http://hdl.handle.net/10016/16040 2008-12-31T23:00:00Z http://hdl.handle.net/10016/8070 Title: Evolving spatial and frequency selection filters for brain-computer interfaces Author(s): Aler, Ricardo; Galván, Inés M.; Valls, José M. Abstract: Abstract—Machine Learning techniques are routinely applied to Brain Computer Interfaces in order to learn a classifier for a particular user. However, research has shown that classiffication techniques perform better if the EEG signal is previously preprocessed to provide high quality attributes to the classifier. Spatial and frequency-selection filters can be applied for this purpose. In this paper, we propose to automatically optimize these filters by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The technique has been tested on data from the BCI-III competition, because both raw and manually filtered datasets were supplied, allowing to compare them. Results show that the CMA-ES is able to obtain higher accuracies than the datasets preprocessed by manually tuned filters. Description: Proceeding of: 2010 IEEE World Congress in Computational Intelligence (WCCI 2010), Barcelona, Spain, July 18-23, 2010 Wed, 30 Jun 2010 22:00:00 GMT http://hdl.handle.net/10016/8070 2010-06-30T22:00:00Z http://hdl.handle.net/10016/6760 Title: Improving classification for brain computer interfaces using transitions and a moving window Author(s): Aler, Ricardo; Galván, Inés M.; Valls, José M. Abstract: The context of this paper is the brain-computer interface (BCI), and in particular the classification of signals with machine learning methods. In this paper we intend to improve classification accuracy by taking advantage of a feature of BCIs: instances run in sequences belonging to the same class. In that case, the classiffication problem can be reformulated into two subproblems: detecting class transitions and determining the class for sequences of instances between transitions. We detect a transition when the Euclidean distance between the power spectra at two different times is larger than a threshold. To tackle the second problem, instances are classified by taking into account, not just the prediction for that instance, but a moving window of predictions for previous instances. Experimental results show that our transition detection method improves results for datasets of two out of three subjects of the BCI III competition. If the moving window is used, classification accuracy is further improved, depending on the window size. Description: Proceeding of: Biosignals 2009. International Conference on Bio-inspired Systems and Signal Processing, BIOSTEC 2009. Porto (Portugal), 14-17 January 2009 Tue, 13 Jan 2009 23:00:00 GMT http://hdl.handle.net/10016/6760 2009-01-13T23:00:00Z http://hdl.handle.net/10016/9251 Title: Using evolutionary multiobjective techniques for imbalanced classifcation data Author(s): García-Rodríguez, Sandra; Aler, Ricardo; Galván, Inés M. Abstract: The aim of this paper is to study the use of Evolutionary Multiobjective Techniques to improve the performance of Neural Net- works (NN). In particular, we will focus on classi¯cation problems where classes are imbalanced. We propose an evolutionary multiobjective ap- proach where the accuracy rate of all the classes is optimized at the same time. Thus, all classes will be treated equally independently of their pres- ence in the training data set. The chromosome of the evolutionary algo- rithm encodes only the weights of the training patterns missclassi¯ed by the NN, instead of all the parameters of the NN as in other approaches. Results show that the multiobjective approach is able to consider all classes at the same time, disregarding to some extent their abundance in the training set or other biases that restrain some of the classes of being learned properly. Description: Proceeding of: Artificial Neural Networks - ICANN 2010. 20th International Conference, Tessaloniki, Greece, September 15-18, 2010. This is an extended version (the paper in the conference proceedings had to be reduced to 10 pages) Tue, 31 Aug 2010 22:00:00 GMT http://hdl.handle.net/10016/9251 2010-08-31T22:00:00Z