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@InProceedings{LacerdaAlmeGalv:2017:AvAcSe,
               author = "Lacerda, Camila Souza dos Anjos and Almeida, Cl{\'a}udia Maria de 
                         and Galv{\~a}o, L{\^e}nio Soares",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Classifica{\c{c}}{\~a}o por {\'a}rvores de decis{\~a}o: 
                         Avalia{\c{c}}{\~a}o de acur{\'a}cia com e sem a 
                         pr{\'e}-sele{\c{c}}{\~a}o de atributos",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6407--6414",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The use of decision trees for image classification has grown 
                         rapidly in recent years, since the reported developments are 
                         promising. The current work uses the C4.5 and Random Forest 
                         methods for selecting statistical and customized attributes 
                         derived from a WorldView-2 (WV-2) image, meant to effectively 
                         separate the classes of interest. The attributes comprising the 
                         bands extracted from transforms such as Principal Component 
                         Analysis and Minimum Noise Fraction in addition to vegetation 
                         indices and attributes based on band ratios were used in the stage 
                         of feature selection. Two datasets have been employed in the 
                         herein described experiments: one consisting of a WV-2 scene with 
                         42 image-derived attributes, and the second one containing the 
                         same WV-2 scene and 28 pre-selected meaningful attributes. 
                         Comparisons between the two datasets showed that for both methods 
                         (C4.5 and RF) the use of statistical attributes plus those derived 
                         from image transforms and arithmetical operations increases the 
                         agreement indices accuracy. Both classifiers work as data miners, 
                         identifying among a large set of attributes those able to 
                         discriminate the concerned classes. The results of this article 
                         comply with the peer-reviewed literature, for they demonstrate 
                         that the classification integrating a greater number of input 
                         attributes (without feature selection) attain a significantly 
                         superior accuracy. In sum, decision tree classifiers have the 
                         capacity to deal with weak explanatory variables, and hence, it is 
                         not possible to assess their importance based on individual 
                         factors alone.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59385",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMCRE",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3PSMCRE",
           targetfile = "59385.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "02 dez. 2020"
}


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