@InProceedings{CamargoBugaSait:2016:AbApAt,
               author = "Camargo, Guilherme and Bugatti, Pedro Henrique and Saito, Priscila 
                         Tiemi Maeda",
          affiliation = "{Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR) e 
                         Universidade Estadual de Campinas (UNICAMP)}",
                title = "Abordagem de Aprendizado Ativo para Classifica{\c{c}}{\~a}o de 
                         Dados Biom{\'e}dicos",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "aprendizado ativo, an{\'a}lise de imagens, 
                         classifica{\c{c}}{\~a}o, imagens biom{\'e}dicas, floresta de 
                         caminhos {\'o}timos.",
             abstract = "A huge volume of biomedical data (images, genes, among others) is 
                         daily generated. The analysis of such data is a complex task that 
                         demands specialized knowledge, and the level of expertise directly 
                         impacts the diagnosis. Besides, due to the volume of data such 
                         task becomes extremely tiresome, and hence highly susceptible to 
                         errors. Trying to solve this problem, machine learning approaches 
                         have been proposed in the literature to perform automatic 
                         classification of such data. Despite the several proposed 
                         techniques, the great majority strictly focus just on the 
                         effectiveness, and relegate the efficiency of the classification. 
                         This paper presents a novel learning approach capable to obtain 
                         high accuracies, as well as maintaining a minimal involvement of 
                         the expert and interactive computational time during the learning 
                         process. To do so, the proposed approach exploits the active 
                         learning paradigm, in order to reduce, organize and select the 
                         most informative samples to the learning process of the pattern 
                         classifier.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3M8SRRE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M8SRRE",
           targetfile = "2016-sibgrapi-wip.pdf",
        urlaccessdate = "2024, May 02"
}