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

  • An e-Learning toolbox based on rule-based fuzzy approaches 

    Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Castro Espinoza, Félix Agustín (Multidisciplinary Digital Publishing Institute, 2020-09-28)
    Article
    Open Access
    In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the ...
  • On-the-fly syntheziser programming with fuzzy rule learning 

    Paz Ortiz, Alejandro Iván; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Romero Merino, Enrique (2020-08-31)
    Article
    Open Access
    This manuscript explores fuzzy rule learning for sound synthesizer programming within the performative practice known as live coding. In this practice, sound synthesis algorithms are programmed in real time by means of ...
  • On the use of pairwise distance learning for brain signal classification with limited observations 

    Calhas, David; Romero Merino, Enrique; Henriques, Rui (2020-05)
    Article
    Restricted access - publisher's policy
    The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes ...
  • To be or nought to be: una qüestió irrellevant? 

    Belanche Muñoz, Luis Antonio (1991-10)
    External research report
    Open Access
  • About the attribute relevance's nature 

    Núñez Esquer, Gustavo; Cortés García, Claudio Ulises; Belanche Muñoz, Luis Antonio; Alvarado Mentado, Matías (1991-03)
    External research report
    Open Access
    The notion of relevance of an attribute in machine learning is of common use in the construction of classfication rules in inductive learning processes. In this work a formal definition of the relevance concept for a given ...
  • Integral seismic risk assessment through fuzzy models 

    González Cardenas, Rubén; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (Multidisciplinary Digital Publishing Institute, 2020-04-26)
    Article
    Open Access
    The usage of indicators as constituent parts of composite indices is an extended practice in many fields of knowledge. Even if rigorous statistical analyses are implemented, many of the methodologies follow simple arithmetic ...
  • 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 ...
  • 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, ...
  • 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 ...
  • 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 ...

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