@InProceedings{RodriguesSouzPapa:2017:PrOpFo,
               author = "Rodrigues, Douglas and Souza, Andr{\'e} Nunes and Papa, Jo{\~a}o 
                         Paulo",
          affiliation = "{Universidade Federal de S{\~a}o Carlos} and {Universidade 
                         Estadual de S{\~a}o Paulo} and {Universidade Estadual de S{\~a}o 
                         Paulo}",
                title = "Pruning Optimum-Path Forest Classifiers Using Multi-Objective 
                         Optimization",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Optimum-Path Forest, Meta-heuristic Multi-objective Optimization, 
                         Prototype Selection.",
             abstract = "Multi-objective optimization plays an important role when one has 
                         fitness functions that are somehow conflicting with each other. 
                         Also, parameter-dependent machine learning techniques can benefit 
                         from such optimization tools. In this paper, we propose a 
                         multi-objective-based strategy approach to build compact though 
                         representative training sets for Optimum-Path Forest (OPF) 
                         learning purposes. Although OPF pruning can provide such a nice 
                         representation, it comes with the price of being 
                         parameter-dependent. The proposed approach cope with that problem 
                         by avoiding the classifier to be hand-tuned by modeling the task 
                         of parameter learning as a multi-objective-oriented optimization 
                         problem, which can be less prone to errors. Experiments on public 
                         datasets show the robustness of the proposed approach, which is 
                         now parameterless and user-friendly.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.23",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.23",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFRFCH",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRFCH",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 02"
}