@InProceedings{LorenaLoreLoreCarv:2014:ClSeAp,
               author = "Lorena, Luiz Henrique Nogueira and Lorena, Ana Carolina and 
                         Lorena, Luiz Antonio Nogueira and Carvalho, Andr{\'e} C. P. L. 
                         F.",
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade de 
                         S{\~a}o Paulo (USP)}",
                title = "Clustering Search applied to Rank Aggregation",
            booktitle = "Proceedings...",
                 year = "2014",
         organization = "Brazilian Conference on Intelligent Systems (BRACIS).",
            publisher = "IEEE",
             keywords = "rank aggregation, Clustering Search.",
             abstract = "Several practical applications require joining various rankings 
                         into a consensus ranking. These applications include gathering the 
                         results of multiple queries in information retrieval, deciding the 
                         result of a poll involving multiple judges and joining the outputs 
                         from ranking classification algorithms. Finding the ranking that 
                         best represents a set of rankings is a NP-hard problem, but a good 
                         solution can be found by using metaheuristics. In this paper, we 
                         investigate the use of Clustering Search (CS) algorithm allied to 
                         Simulated Annealing (SA) for solving the rank aggregation problem. 
                         CS will clusters the solutions found by SA in order to find 
                         promising regions in the search space, that can be further 
                         exploited by a local search. Experimental results on benchmark 
                         data sets show the potential of this approach to find a consensus 
                         ranking, achieving similar or better solutions than those found by 
                         other popular rank aggregation strategies.",
  conference-location = "S{\~a}o Carlos",
      conference-year = "2014",
                label = "lattes: 7195702087655314 3 LorenaLoreLoreCarv:2014:ClSeAp",
             language = "en",
           targetfile = "lorena_clustering.pdf",
        urlaccessdate = "25 abr. 2024"
}