GP Ensembles for Large Scale Data Classification (Articolo in rivista)

Type
Label
  • GP Ensembles for Large Scale Data Classification (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Alternative label
  • Gianluigi Folino; Clara Pizzuti ; Giandomenico Spezzano (2006)
    GP Ensembles for Large Scale Data Classification
    in IEEE transactions on evolutionary computation
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gianluigi Folino; Clara Pizzuti ; Giandomenico Spezzano (literal)
Pagina inizio
  • 604 (literal)
Pagina fine
  • 616 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 10-5 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • DBLP (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di calcolo e reti ad alte prestazioni Istituto di calcolo e reti ad alte prestazioni Istituto di calcolo e reti ad alte prestazioni (literal)
Titolo
  • GP Ensembles for Large Scale Data Classification (literal)
Abstract
  • An extension of Cellular Genetic Programming for data classification (CGPC) to induce an ensemble of predictors is presented. Two algorithms implementing the bagging and boost- ing techniques are described and compared with CGPC. The approach is able to deal with large data sets that do not fit in main memory since each classifier is trained on a subset of the overall training data. The predictors are then combined to classify new tuples. Ex- periments on several data sets show that, by using a training set of reduced size, better classification accuracy can be obtained, but at a much lower computational cost. (literal)
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