SOCO has carried out work on Feature Selection (FS) both for supervised and unsupervised models: An innovative software for FS in supervised models was developed after an exhaustive review of methods in the Machine Learning literature. It includes a synthetic data generator, an algorithm simulator and an automatic results quality evaluator. SOCO is currently working on a sequential algorithms simulator. Techniques for FS are being developed for Supervised Neural Networks and Support Vector Machines. As for the unsupervised methods, we are currently tackling the less common problem of feature selection in data clustering with mixture models. One of the goals of this type of FS is making compatible the assessment of feature relevance with the improvement of the interpretability of the clustering results through visualization.
The first five describe methodologies with a similar goal: the resolution of complex problems that cannot be efficiently solved by means of traditional computational methods (hard computing). Feature Selection and Extraction deals with problems of data dimensionality reduction that are present in all the previous lines. Finally, Pattern Recognition and Computer Vision copes with a huge application field where the performance of AI systems is usually worse than the human one, and where the soft computing techniques have great potential. Related to it, Data and Knowledge Visualization concerns ways in which Soft Computing complements human vision in problems of exploratory Data Mining.
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Until 2008, SOCO focused much of its applied research to problems of failure detection in Waste Water Treatment Plants (WWTPs) and exploration of the ecological status of European streams, in collaboration with several partners, including the Laboratori d'Enginyeria Química i Ambiental (LEQUIA) at Universitat de Girona and the and Centre d'Estudis Avançats de Blanes-CSIC at Girona. A Long-term collaboration with the Water Microbiology Group - MARS, Dept. of Microbiology of the University of Barcelona (UB), was started in 2001 within the European project TOFPSW ("Tracking the origin of faecal pollution in surface water", EVK1-CT-2000-00080). It was continued within the MICINN projects "Determinación del origen de la contaminación fecal en el agua" (CGL2004-04702-C02), from 2004 to 2007, and "Procedimientos para la discriminación de origen de la contaminación fecal animal en el agua" (CGL2007-65980-C02-01/HID), from 2008 to 2011. Currently we undertake external assessment to the European project Aquavalens (go) "Protecting the health of Europeans by improving methods for the detection of pathogens in drinking water and water used in food preparation", from 2013 to 2018. In seismic data analysis, achieving integrated risk modeling is a difficult undertaking because interconnections between the assumed risk-related variables are still not completely understood. Current research of SOCO in this area involves the development of a holistic seismic risk fuzzy inference system.