L'objectiu principal del grup és avançar en les metodologies que conformen l'àrea de la infor-màtica tova (Soft Computing), així com investigar possibles hibridacions d'aquestes amb l'ob-jectiu de millorar-ne el rendiment i la fiabilitat. També és un objectiu important aplicar les metodologies desenvolupades a problemes reals en àrees com la medicina, ecologia, farmacoprote-òmica, e-learning, etc. El grup ha publicat més de 150 articles de revista, més de 300 articles en congressos, 50 capítols de llibres i 60 reports de recerca.

http://futur.upc.edu/SOCO

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

http://futur.upc.edu/SOCO

Recent Submissions

  • Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques 

    González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Gámez Moreno, María G.; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E.; López Morteo, Gabriel A. (2015-12-01)
    Article
    Open Access
    Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic ...
  • Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks 

    Romero Merino, Enrique; Alquézar Mancho, René (2010-06)
    External research report
    Open Access
    Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one ...
  • Understanding (dis)similarity measures 

    Belanche Muñoz, Luis Antonio (2013-01)
    External research report
    Open Access
    Intuitively, the concept of similarity is the notion to measure an inexact matching between two entities of the same reference set. The notions of similarity and its close relative dissimilarity are widely used in many ...
  • Visual-FIR for ozone modeling and prediction 

    Nebot Castells, M. Àngela; Múgica, Violeta; Escobet Canal, Antoni (2007-04)
    External research report
    Open Access
    Air pollution is one of the most important environmental problems in urban areas, being extremely critical in Mexico City. The main air pollution problem that has been identified in Mexico City metropolitan area is the ...
  • Modelado de las concentraciones locales de ozono en la Zona Centro del Area Metropolitana de la Ciudad de México 

    Acosta, Jesús; Nebot Castells, M. Àngela; Fuertes Armengol, José Mª (2006-03)
    External research report
    Open Access
    La contaminación del aire constituye el problema medioambiental de principal atención en las áreas urbanas debido a que afecta la salud de la población, en especial a la de los niños. Es por ello, que la construcción de ...
  • A variational formulation for GTM through time: Theoretical foundations 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2007-10)
    External research report
    Open Access
    Generative Topographic Mapping (GTM) is a latent variable model that, in its standard version, was conceived to provide clustering and visualization of multivariate, real-valued, i.i.d. data. It was also extended to deal ...
  • A variational Bayesian formulation for GTM: Theoretical foundations 

    Olier Caparroso, Iván; Vellido Alcacena, Alfredo (2007-09)
    External research report
    Open Access
    Generative Topographic Mapping (GTM) is a non-linear latent variable model of the manifold learning family that provides simultaneous visualization and clustering of high-dimensional data. It was originally formulated as ...
  • Predictive models in churn data mining: a review 

    García, David L.; Vellido Alcacena, Alfredo; Nebot Castells, M. Àngela (2007-01)
    External research report
    Open Access
    The development of predictive models of customer abandonment plays a central role in any churn management strategy. These models can be developed using either qualitative approaches or can take a data-centred point of view. ...
  • Customer continuity management as a foundation for churn data mining 

    García, David L.; Vellido Alcacena, Alfredo; Nebot Castells, M. Àngela (2007-01)
    External research report
    Open Access
    This report lays the first theoretical foundations for a research program on analytical churn management. In the current hypercompetitive business scenario, firms have to bend over backwards in their strategies both to ...
  • Elements of generative manifold learning for semi-supervised tasks 

    Cruz, Raúl; Vellido Alcacena, Alfredo (2007-01)
    External research report
    Open Access
    For many real-world application problems, the availability of data labels for supervised learning is rather limited. It is often the case that a limited number of labelled cases is accompanied by a larger number of unlabeled ...

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