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@InProceedings{FariaSanSarRocTor:2013:WhFeMo,
               author = "Faria, Fabio Augusto and Santos, Jefersson Alex dos and Sarkar, 
                         Sudeep and Rocha, Anderson and Torres, Ricardo da Silva",
          affiliation = "{University of Campinas} and {University of Campinas} and 
                         {University of South Florida} and {University of Campinas} and 
                         {University of Campinas}",
                title = "Classifier Selection based on the Correlation of Diversity 
                         Measures: When Fewer is More",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
                         Claudio",
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "multiple classifier system, ensemble of classifiers, diversity 
                         measures, coffee crop recognition.",
             abstract = "The ever-growing access to high-resolution images has prompted the 
                         development of region-based classification methods for remote 
                         sensing images. However, in agricultural applications, the 
                         recognition of specific regions is still a challenge as there 
                         could be many different spectral patterns in a same studied area. 
                         In this context, depending on the features used, different 
                         learning methods can be used to create complementary classifiers. 
                         Many researchers have developed solutions based on the use of 
                         machine learning techniques to address these problems. Examples of 
                         successful initiatives are those dedicated to the development of 
                         learning techniques for data fusion or Multiple Classifier Systems 
                         (MCS). In MCS, diversity becomes an essential factor for their 
                         success. Different works have been using diversity measures to 
                         select appropriate high-performance classifiers, but the challenge 
                         of finding the optimal number of classifiers for a target task has 
                         not been properly addressed yet. In general, the proposed 
                         solutions rely on the a priori use of ad hoc strategies for 
                         selecting classifiers, followed by the evaluation of their 
                         effectiveness results during training. Searching by the optimal 
                         number of classifiers, however, makes the selection process more 
                         expensive. In this paper, we address this issue by proposing a 
                         novel strategy for selecting classifiers to be combined based on 
                         the correlation of different diversity measures. Diversity 
                         measures are used to rank pairs of classifiers and the agreement 
                         among ranked lists guides the classifier selection process. A 
                         fusion framework has been used in our experiments targeted to the 
                         classification of coffee crops in remote sensing images. 
                         Experiment results demonstrate that the novel strategy is able to 
                         yield comparable effectiveness results when contrasted to several 
                         baselines, but using much fewer classifiers.",
  conference-location = "Arequipa, Peru",
      conference-year = "Aug. 5-8, 2013",
             language = "en",
           targetfile = "sibgrapi-2013-camera-ready-paper-114613.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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