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@InProceedings{AnochiCamp:2015:DiReUs,
               author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Dimensionality reduction using rough set approach for climate 
                         prediction",
            booktitle = "Anais...",
                 year = "2015",
                pages = "181--185",
         organization = "International Conference on Applied Computing, 12.",
            publisher = "IADIS: International Association for Development of the 
                         Information Society",
             keywords = "Data mining, Rough sets theory, Optimal neural network, MPMaynooth 
                         (Irlanda) Lisboa (Portugal)CA: multi-particle collision algorithm, 
                         Climate prediction.",
             abstract = "In this article, a data mining method to variables selection for 
                         climate prediction is presented. The data were processed by Rough 
                         Set Theory to extract relevant information to perform the seasonal 
                         climate prediction by neural network for the South of Brazil, with 
                         a reduced data set. The neural network was self-configured by MPCA 
                         metaheuristic. Two experiments were conducted with neural network: 
                         complete meteorological input variables, and reduced data set 
                         extract from the rough set theory.",
  conference-location = "Maynooth",
      conference-year = "24-26 Oct.",
                 isbn = "9789898533456",
                label = "lattes: 5142426481528206 2 AnochiCamp:2015:DiReUs",
             language = "en",
           targetfile = "1_anochi.pdf",
        urlaccessdate = "01 dez. 2020"
}


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