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@InProceedings{NakamuraPeCoRoPaYa:2012:BiBaAl,
               author = "Nakamura, Rodrigo Yuji Mizobe and Pereira, Luis Augusto Martins 
                         and Costa, Kelton Augusto Pontara da and Rodrigues, Douglas and 
                         Papa, Joao Paulo and Yang, Xin-She",
          affiliation = "{Sao Paulo State University - UNESP} and {Sao Paulo State 
                         University - UNESP} and {Sao Paulo State University - UNESP} and 
                         {Sao Paulo State University - UNESP} and {Sao Paulo State 
                         University - UNESP} and {National Physical Laboratory}",
                title = "BBA: A Binary Bat Algorithm for Feature Selection",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno, 
                         Roberto and Silva, Luciano",
         organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "feature selection, bat algorithm, optimum-path forest.",
             abstract = "Feature selection aims to find the most important information from 
                         a given set of features. As this task can be seen as an 
                         optimization problem, the combinatorial growth of the possible 
                         solutions may be inviable for a exhaustive search. In this paper 
                         we propose a new nature-inspired feature selection technique based 
                         on the bats behavior, which has never been applied to this context 
                         so far. The wrapper approach combines the power of exploration of 
                         the bats together with the speed of the Optimum-Path Forest 
                         classifier to find the set of features that maximizes the accuracy 
                         in a validating set. Experiments conducted in five public datasets 
                         have demonstrated that the proposed approach can outperform some 
                         well-known swarm-based techniques.",
  conference-location = "Ouro Preto",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "updated_paper.pdf",
        urlaccessdate = "2021, Jan. 24"
}


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