Listar Departamento de Informática y Análisis Numérico por autor "51050f46-e39c-44f8-8200-ef279940acba"

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    • A general framework for boosting feature subset selection algorithms 

      Pérez Rodríguez, Javier; Haro García, Aída de; Romero-del-Castillo, Juan A.; García Pedrajas, Nicolás (Elsevier, 2018)
      Feature selection is one of the most important tasks in many machine learning and data mining problems. Due to the increasing size of the problems, removing useless, erroneous or noisy features is frequently an initial ...

    • CRIBEL: Lifelong learning social network governed by academic institutions: an affordable serverless model in the cloud 

      Romero-del-Castillo, Juan A.; Mancha-Dieguez, J.; Ortiz-Boyer, Domingo (IATED, 2023)
      Academic institutions, teachers, students and workers are facing important changes in the way they organise work and training in new skills demanded by companies in the face of the new challenges of society in general ...

    • Higher education institutions to promote a lifelong learning strategy 

      Romero-del-Castillo, Juan A.; Haro García, Aída de; Ortiz-Boyer, Domingo (IATED, 2019)
      In today’s society students and graduates are demanded to be equipped with two fundamental attitudes. The first is being active learners enrolled in all different activities related to Active Learning that have place at ...

    • Instance selection for multi-label learning based on a scalable evolutionary algorithm 

      Romero-del-Castillo, Juan A.; Ortiz-Boyer, Domingo; García Pedrajas, Nicolás (IEEE, 2021)
      Multi-label classification has recently attracted greater research interest as a data mining task. Many current ap- plications in data mining address problems having instances that belong to more than one class, which ...

    • Local-based k values for multi-label k-nearest neighbors rule 

      Romero-del-Castillo, Juan A.; Mendoza-Hurtado, Manuel; Ortiz-Boyer, Domingo; García Pedrajas, Nicolás (Elsevier, 2022)
      Multi-label learning is a growing field in machine learning research. Many applications address instances that simultaneously belong to many categories, which cannot be disregarded if optimal results are desired. Among the ...

    • SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features 

      García Pedrajas, Nicolás; Romero-del-Castillo, Juan A.; Cerruela García, Gonzalo (Elsevier, 2021)
      Data reduction is becoming increasingly relevant due to the enormous amounts of data that are constantly being produced in many fields of research. Instance selection is one of the most widely used methods for this task. ...