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.

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)

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)

Recent Submissions

  • Male and female politicians on Twitter: A machine learning approach 

    Beltran Jorba, Javier; Gallego Dobón, Aina; Huidobro Torres, Alba; Romero Merino, Enrique; Padró, Lluís (2020-03-17)
    Article
    Restricted access - publisher's policy
    How does the language of male and female politicians differ when they communicate directly with the public on social media? Do citizens address them differently? We apply Lasso logistic regression models to identify the ...
  • On some strategies for missing values in positive semidefinite matrices 

    Belanche Muñoz, Luis Antonio; Vázquez García, Miguel (Thompson, 2005)
    Conference report
    Open Access
    This article presents our work on missing values in Positive Semi-Definite or PSD matrices. We show how simple properties of PSD matrices can be used to deal with missing values. We study several situations and investigate ...
  • Similarity-based heterogeneous neuron models 

    Belanche Muñoz, Luis Antonio (IOS Press, 2000)
    Conference report
    Open Access
    This paper introduces a general class of neuron models, accepting heterogeneous inputs in the form of mixtures of continuous (crisp or fuzzy) numbers, linguistic information, and discrete (either ordinal or nominal) ...
  • Fuzzy inputs and missing data in similarity-based heterogeneous neural networks 

    Belanche Muñoz, Luis Antonio; Valdés Ramos, Julio José (Springer, 1999)
    Conference report
    Open Access
    Fuzzy heterogeneous networks are recently introduced neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, ...
  • A thermodynamic algorithm for feature selection 

    Belanche Muñoz, Luis Antonio; González Navarro, Félix Fernando (Thomson Editores Spain, 2007)
    Conference report
    Open Access
    The main purpose of Feature Selection (FS) is to find a reduced subset of attributes from a data set described by a feature set. This implies a search process in the space of possible solutions, trying to optimize an ...
  • Un algoritmo para el cálculo de la relevancia entrópica multivariada y su uso en la selección de variables 

    González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (Thomson Editores Spain, 2007)
    Conference report
    Open Access
    La reducción de la dimensionalidad mediante la selección de variables es uno de los pasos fundamentales del preprocesado de datos, como fase previa al análisis de información y descubrimiento de conocimiento. De entre los ...
  • Feature selection in proton magnetic resonance spectroscopy for brain tumor classification 

    González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (2008)
    Conference report
    Open Access
    H-MRS is a technique that uses response of protons under certain magnetic conditions to reveal the biochemical structure of human tissue. An important application is found in brain tumor diagnosis, due to the known ...
  • Using fuzzy heterogeneous neural networks to learn a model of the central nervous system control 

    Belanche Muñoz, Luis Antonio; Valdés Ramos, Julio José (Verlag Mainz, 1998)
    Conference report
    Open Access
    Fuzzy heterogeneous networks based on similarity are recently introduced feed-forward neural network models composed by neurons of a general class whose inputs are mixtures of continuous (crisp and/or fuzzy) with discrete ...
  • TFS: a thermodynamical search algorithm for feature subset selection 

    González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio (Thomson Editores Spain, 2007)
    Conference report
    Open Access
    This work tackles the problem of selecting a subset of features in an inductive learning setting, by introducing a novel Thermodynamic Feature Selection algorithm (TFS). Given a suitable objective function, the algorithm ...
  • Remainder subset awareness for feature subset selection 

    Prat Masramon, Gabriel; Belanche Muñoz, Luis Antonio (Thomson Editores Spain, 2007)
    Conference report
    Open Access
    Feature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. The family of algorithms known as plus-l-minus-r ...
  • Modeling the input-output behaviour of wastewater treatment plants using soft computing techniques 

    Belanche Muñoz, Luis Antonio; Valdes Ramos, Julio José; Comas Matas, Joaquim; Rodriguez Roda, Ignasi; Poch Espallargas, Manel (1998)
    Conference report
    Open Access
    Wastewater Treatment Plants (WWTPs) control and prediction under a wide range of operating conditions is an important goal in order to avoid breaking of environmental balance, keep the system in stable operating conditions ...
  • On the selection of hidden neurons with heuristic search strategies for approximation 

    Barrio Moliner, Ignacio; Romero Merino, Enrique; Belanche Muñoz, Luis Antonio (2006)
    Conference report
    Open Access
    Feature Selection techniques usually follow some search strategy to select a suitable subset from a set of features. Most neural network growing algorithms perform a search with Forward Selection with the objective of ...

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