@Article{PessoaStep:2014:InApAt,
               author = "Pessoa, Alex Sandro Aguiar and Stephany, Stephan",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "An Innovative Approach for Attribute Reduction in Rough Set 
                         Theory",
              journal = "Intelligent Information Management",
                 year = "2014",
               volume = "6",
               number = "5",
                pages = "223--239",
                 note = "Setores de Atividade: Administra{\c{c}}{\~a}o p{\'u}blica, 
                         defesa e seguridade social.",
             keywords = "sele{\c{c}}{\~a}o de atributos, minera{\c{c}}{\~a}o de dados, 
                         conjuntos aproximativos.",
             abstract = "The Rough Sets Theory is used in data mining with emphasis on the 
                         treatment of uncertain or vague information. In the case of 
                         classification, this theory implicitly calculates reducts of the 
                         full set of attributes, eliminating those that are redundant or 
                         meaningless. Such reducts may even serve as input to other 
                         classifiers other than Rough Sets. The typical high dimensionality 
                         of current databases precludes the use of greedy methods to find 
                         optimal or suboptimal reducts in the search space and requires the 
                         use of stochastic methods. In this context, the calculation of 
                         reducts is typically performed by a genetic algorithm, but other 
                         metaheuristics have been proposed with better performance. This 
                         work proposes the innovative use of two known metaheuristics for 
                         this calculation, the Variable Neighborhood Search, the Variable 
                         Neighborhood Descent, besides a third heuristic called Decrescent 
                         Cardinality Search. The last one is a new heuristic specifically 
                         proposed for reduct calculation. Considering some databases 
                         commonly found in the literature of the area, the reducts that 
                         have been obtained present lower cardinality, i.e., a lower number 
                         of attributes.",
                  doi = "10.4236/iim.2014.65022",
                  url = "http://dx.doi.org/10.4236/iim.2014.65022",
                 issn = "2150-8194",
                label = "lattes: 1446664587151293 2 PessoaStep:2014:InApAt",
             language = "pt",
        urlaccessdate = "27 abr. 2024"
}