A main research line at SOCO concerns Fuzzy Inductive Reasoning (FIR): a model-based qualitative methodology, derived from Systems General Theory, which is quite attractive for dynamic systems modelling and prediction. Among its advantages, it is worth mentioning that inductive reasoning allows dealing with time in qualitative models as a continuous variable. The technique can be applied to any system amenable to observation and experimentation. Furthermore, the methodology∆s simulation engine has an inherent evaluation mechanism that prevents it from reaching to conclusions that cannot be justified by evidence. This methodology has been designed in collaboration with the University of Arizona [go]. FIR has been applied to the development of a Fault Monitoring System.
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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Members of SOCO have also been involved in applied research on social sciences, including