@InProceedings{PereiraCostJr:2021:DeImHi,
               author = "Pereira, Rodolfo Miranda and Costa, Yandre Maldonado e Gomes da 
                         and Jr. , Carlos Nascimento Silla",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Paran{\'a} 
                         (PUCPR) e Instituto Federal do Paran{\'a} (IFPR)} and 
                         {Universidade Estadual de Maring{\'a} (UEM)} and 
                         {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Paran{\'a} 
                         (PUCPR)}",
                title = "Dealing with Imbalanceness in Hierarchical Classification Problems 
                         Through Data Resampling",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "hierarchical classification, class imbalance, resampling 
                         algorithms.",
             abstract = "Many important classification problems are imbalanced. Although 
                         resampling approaches are a common solution for different types of 
                         classification problems, they were still not defined for 
                         hierarchical classification problems. The objective of this work 
                         is to propose novel resampling approaches to handle the class 
                         imbalanceness issue in hierarchical classification problems. Four 
                         directions were investigated: (i) The use of classic resampling 
                         methods; (ii) A label path conversion strategy; (iii) The design 
                         of schemas to use resampling algorithms with local approaches; 
                         (iv) The proposal of global resampling algorithms. To show the 
                         impacts of the contribution of this work, we have investigated the 
                         imbalanceness issue in the COVID-19 identification in chest x-ray 
                         images.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45D9PFB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45D9PFB",
           targetfile = "WTD_SIBGRAPI_2021_Camera_Ready.pdf",
        urlaccessdate = "2024, May 06"
}