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

  • The importance of interpretability and visualization in ML for medical applications 

    Vellido Alcacena, Alfredo (2021)
    Conference lecture
    Open Access
    Many areas of science have made a sharp transition towards data-dependent methods, enabled by simultaneous advances in data acquisition and the development of networked system technologies. This is particularly clear in ...
  • Informàtica bàsica II, ETSEIB. Memòria del curs 1994-1995 

    Vila Marta, Sebastià; Pla García, Núria; Soto Riera, Antoni; Pérez Vidal, Lluís; Roura Ferret, Salvador; Franquesa Niubó, Marta; Cotrina Navau, Josep; Alquézar Mancho, René; Martínez Parra, Conrado (1995-02-06)
    Research report
    Open Access
    Memòria de l'assignatura d'Infomàtica bàsica II del curs 94-95.
  • LONG-REMI: An AI-based technological application to promote healthy mental longevity grounded in reminiscence therapy 

    Nebot Castells, M. Àngela; Domenech Pou, Sara; Albino-Pires, Natália; Múgica Álvarez, Francisco; Benali, Anass; Porta, Xènia; Nebot Mugica, Oriol; Santos, Pedro M. (2022-05-15)
    Article
    Open Access
    Reminiscence therapy (RT) consists of thinking about one’s own experiences through the presentation of memory-facilitating stimuli, and it has as its fundamental axis the activation of emotions. An innovative way of offering ...
  • The coming of age of interpretable and explainable machine learning models 

    Lisboa, Paulo; Saralajew, Sascha; Vellido Alcacena, Alfredo; Villmann, Thomas (I6doc.com, 2021)
    Conference report
    Open Access
    Machine learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm. In the wake of this realisation, strict legal regulations for these systems are currently being ...
  • Tracking a well diversified portfolio with maximum entropy in the mean 

    Arratia Quesada, Argimiro Alejandro; Gzyl, Henryk; Mayoral Blaya, Silvia (Multidisciplinary Digital Publishing Institute (MDPI), 2022-02)
    Article
    Open Access
    In this work we address the following problem: Having chosen a well diversified portfolio, we show how to improve on its return, maintaining the diversification. In order to achieve this boost on return we construct a ...
  • Misreported longitudinal data in epidemiology: review of mixture-based advances and current challenges 

    Moriña, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Puig Casado, Pere (2021-12)
    Article
    Open Access
    The problem of dealing with misreported data is very common in a wide range of contexts and for different reasons. This has been and still is an important issue for data analysts and statisticians as not accounting for it ...
  • Forest fire forecasting using fuzzy logic models 

    Nebot Castells, M. Àngela; Múgica Álvarez, Francisco (2021-07-29)
    Article
    Open Access
    In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is ...
  • Off-the-grid: Fast and effective hyperparameter search for kernel clustering 

    Ordozgoiti Rubio, Bruno; Belanche Muñoz, Luis Antonio (Springer, 2020)
    Conference report
    Open Access
    Kernel functions are a powerful tool to enhance the k-means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dramatic impact on the result. In supervised ...
  • Predicciones financieras basadas en análisis de sentimiento de textos y minería de opiniones 

    Arratia Quesada, Argimiro Alejandro (FUNCAS, 2021-04-01)
    Part of book or chapter of book
    Restricted access - publisher's policy
    En este capítulo se describe la mecánica básica para construir un modelo de predicción que utiliza indicadores de sentimiento derivados de datos textuales. Enfocamos nuestro objetivo de predicciones en series de ...
  • Cumulated burden of Covid-19 in Spain from a Bayesian perspective 

    Moriña, David; Fernandez Fontelo, Amanda; Cabaña Nigro, Ana Alejandra; Arratia Quesada, Argimiro Alejandro; Ávalos Villaseñor, Gustavo Eduardo; Puig, Pedro (2021-12)
    Article
    Open Access
    Background The main goal of this work is to estimate the actual number of cases of Covid-19 in Spain in the period 01-31-2020/06-01-2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately ...
  • kernInt: A kernel framework for integrating supervised and unsupervised analyses in spatio-temporal metagenomic datasets 

    Ramon Gurrea, Elies; Belanche Muñoz, Luis Antonio; Molist Gasa, Francesc; Quintanilla Aguado, Raquel; Pérez Enciso, Miguel; Ramayo Caldas, Yuliaxis (2021-01-28)
    Article
    Open Access
    The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype ...
  • Sentiment analysis of financial news: mechanics and statistics 

    Arratia Quesada, Argimiro Alejandro; Ávalos Villaseñor, Gustavo Eduardo; Cabaña Nigro, Ana Alejandra; Duarte López, Ariel; Renedo Mirambell, Martí (Springer, 2021-06-11)
    Part of book or chapter of book
    Open Access
    This chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, ...

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