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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SOCO currently works in a number of applications of these methodologies, which are summarized next:
Members of SOCO have also been involved in applied research on social sciences, including
E-learning: Many real e-learning projects fall short of their expectations, as too much time is required not only in the process of providing feedback to the virtual learners, but also in the evaluation proces. Current research of SOCO in this area entails the development of a platform that allows the identification of students' learning behaviour models that allow both students and teachers to predict students' performance based on the current learning behaviour.
Music: Algorithmic composition is the process of create music by means of formalizable methods. Despite intensive research, many system outputs fail to produce specific responses in the listener, known as high-level musical features (e.g., expressiveness or coherence, emotion, personality, etc). SOCO's research in this area involves the design and implementation of a methodology for the modeling of high level musical features.