@InProceedings{SatoShimKuplGome:2013:AnCoAl,
               author = "Sato, Luciane Yumie and Shimabukuro, Yosio Edemir and Kuplich, 
                         Tatiana Mora and Gomes, Vitor Conrado Faria",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise comparativa de algoritmos de {\'a}rvore de 
                         decis{\~a}o do sistema WEKA para classifica{\c{c}}{\~a}o do uso 
                         e cobertura da terra",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2353--2360",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "In the last years, the data mining techniques are increasingly 
                         used for classification purposes, and between several techniques 
                         it is highlighted the decision tree. This tool improves the 
                         accuracy of classification, and also allows the integration of 
                         different data types in the classification. Thus, this work has as 
                         main objective to analyze and compare the best data mining 
                         algorithm of decision tree available in WEKA software to use for 
                         land use and land cover classification in the Tapajos National 
                         Forest region. For this, we used a Landsat-5/TM image, the 
                         fraction images obtained by the Linear Spectral Mixture Model, the 
                         Normalized Difference Vegetation Index, the Normalized Water Index 
                         and the Soil-Adjusted Vegetation Index as input data for the 
                         creation of the decision trees. To define the best algorithm, the 
                         total size of the decision tree, the number of leaves, the time 
                         taken for the creation of the decision tree the number of pixels 
                         correctly classified, the number of incorrectly classified pixels 
                         and Kappa were considered. The algorithm that presented the best 
                         results and that best described the classes of land use and land 
                         cover of the study area was SimpleCart algorithm, that is an 
                         implementation of the Classification and Regression Tress 
                         algorithm. The decision tree technique showed satisfactory results 
                         in the classification of the images and the results were generated 
                         quickly, showing the computational efficiency of this technique.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "734",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GFLK",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GFLK",
           targetfile = "p0734.pdf",
                 type = "Classifica{\c{c}}{\~a}o e Minera{\c{c}}{\~a}o de Dados",
        urlaccessdate = "28 abr. 2024"
}