Now showing items 1-7 of 7

    • Desarrollo de un mecanismo de combinación de clasificadores basado en los vectores de salidas más cercanas 

      Jorge Soteras, Mikel Aingeru (2014)   Trabajo Fin de Grado/Gradu Amaierako Lana  OpenAccess
      Este proyecto consiste en desarrollar un nuevo modelo de combinación de clasificadores basado en la similitud entre las salidas que se obtienen entre el ejemplo a clasificar y los ejemplos de entrenamiento. Es decir, ...
    • An empirical study on supervised and unsupervised fuzzy measure construction methods in highly imbalanced classification 

      Uriz Martín, Mikel Xabier Upna; Paternain Dallo, Daniel Upna Orcid; Bustince Sola, Humberto Upna Orcid; Galar Idoate, Mikel Upna Orcid (IEEE, 2020)   Contribución a congreso / Biltzarrerako ekarpena  OpenAccess
      The design of an ensemble of classifiers involves the definition of an aggregation mechanism that produces a single response obtained from the information provided by the classifiers. A specific aggregation methodology ...
    • An evolutionary underbagging approach to tackle the survival prediction of trauma patients: a case study at the Hospital of Navarre 

      Sanz Delgado, José Antonio Upna Orcid; Galar Idoate, Mikel Upna Orcid; Bustince Sola, Humberto Upna Orcid; Belzunegui Otano, Tomás Upna Orcid (IEEE, 2019)   Artículo / Artikulua  OpenAccess
      Survival prediction systems are used among emergency services at hospitals in order to measure their quality objectively. In order to do so, the estimated mortality rate given by a prediction model is compared with the ...
    • INFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity control 

      Sáez, José Antonio; Galar Idoate, Mikel Upna Orcid; Luengo, Julián; Herrera, Francisco (Elsevier, 2015)   Artículo / Artikulua  OpenAccess
      In classification, noise may deteriorate the system performance and increase the complexity of the models built. In order to mitigate its consequences, several approaches have been proposed in the literature. Among them, ...
    • A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models 

      Galar Idoate, Mikel Upna Orcid; Derrac, Joaquín; Peralta, Daniel; Triguero, Isaac; Paternain Dallo, Daniel Upna Orcid; López Molina, Carlos Upna Orcid; García, Salvador; Benítez, José Manuel; Pagola Barrio, Miguel Upna Orcid; Barrenechea Tartas, Edurne Upna Orcid; Bustince Sola, Humberto Upna Orcid; Herrera, Francisco (Elsevier, 2015)   Artículo / Artikulua  OpenAccess
      This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point ...
    • A survey of fingerprint classification Part II: experimental analysis and ensemble proposal 

      Galar Idoate, Mikel Upna Orcid; Derrac, Joaquín; Peralta, Daniel; Triguero, Isaac; Paternain Dallo, Daniel Upna Orcid; López Molina, Carlos Upna Orcid; García, Salvador; Benítez, José Manuel; Pagola Barrio, Miguel Upna Orcid; Barrenechea Tartas, Edurne Upna Orcid; Bustince Sola, Humberto Upna Orcid; Herrera, Francisco (Elsevier, 2015)   Artículo / Artikulua  OpenAccess
      In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the ...
    • Unsupervised fuzzy measure learning for classifier ensembles from coalitions performance 

      Uriz Martín, Mikel Xabier Upna; Paternain Dallo, Daniel Upna Orcid; Domínguez Catena, Iris Upna Orcid; Bustince Sola, Humberto Upna Orcid; Galar Idoate, Mikel Upna Orcid (IEEE, 2020)   Artículo / Artikulua  OpenAccess
      In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: ...