An Application of Support Vector Machine in Bioinformatics: Automated Recognition of Epileptiform Patterns in EEG Using SVM Classifier Designed by a Perturbation Method.
Conference: Advances in Information Systems, Third International Conference, ADVIS 2004, Izmir, Turkey, October 20-22, 2004, Proceedings
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ABSTRACT: Feature selection is required in many applications that involve high dimensional model building or classification problems. Many bioinformatics applications belong to this type. Recently, some approaches for supervised and unsupervised feature selection as a multi-objective optimization problem have been proposed. As the performance of unsupervised classification is evaluated through the quality of the obtained groups or clusters in the data set to be classified, it is difficult to define a suitable objective function that drives the selection of the features. Thus, several evaluation measures, and thus multi-objective clustering characterization, could provide a suitable set of features for unsupervised classification. In this paper, we consider the parallel implementation of a multi-objective feature selection that makes it possible to apply it to complex classification problems such as those having many features to select, and specifically high-dimensional data sets with much more features than data items. In this paper, we propose master-worker implementations of two different parallel evolutionary models, the parallel computation of the cost functions for the individuals in the population, and the parallel execution of evolutionary multi-objective procedures on subpopulations. The experiments accomplished on different benchmarks, including some related with feature selection in classification of EEG (Electroencephalogram) signals for BCI (Brain Computer Interface) applications, show the benefits of parallel processing not only for decreasing the running time, but also for improving the solution quality.Cluster Computing (CLUSTER), 2014 IEEE International Conference on, Madrid (Spain); 09/2014
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ABSTRACT: Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain-Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile.Expert Systems with Applications 02/2015; 42(9). DOI:10.1016/j.eswa.2015.01.061 · 1.97 Impact Factor
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