Improving lazy attribute selection.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorPereira, Rafael Barros-
Autor(es): dc.creatorPlastino, Alexandre-
Autor(es): dc.creatorZadrozny, Bianca-
Autor(es): dc.creatorMerschmann, Luiz Henrique de Campos-
Autor(es): dc.creatorFreitas, Alex Alves-
Data de aceite: dc.date.accessioned2019-11-06T13:32:03Z-
Data de disponibilização: dc.date.available2019-11-06T13:32:03Z-
Data de envio: dc.date.issued2015-01-26-
Data de envio: dc.date.issued2015-01-26-
Data de envio: dc.date.issued2011-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/4385-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/557435-
Descrição: dc.descriptionAttribute selection is a data preprocessing step which aims at identifying relevant attributes for a target data mining task – specifically in this article, the classification task. Previously, we have proposed a new attribute selection strategy – based on a lazy learning approach – which postpones the identification of relevant attributes until an instance is submitted for classification. Experimental results showed the effectiveness of the technique, as in most cases it improved the accuracy of classification, when compared with the analogous eager attribute selection approach performed as a data preprocessing step. However, in the previously proposed approach, the performance of the classifier depends on the number of attributes selected, which is a user-defined parameter. In practice, it may be difficult to select a proper value for this parameter, that is, the value that produces the best performance for the classification task. In this article, aiming to overcome this drawback, we propose two approaches to be used coupled with lazy attribute selection technique: one that tries to identify, in a wrapper-based manner, the appropriate number of attributes to be selected and another that combines, in a voting approach, different numbers of attributes. Experimental results show the effectiveness of the proposed techniques. The assessment of these approaches confirms that the lazy learning paradigm can be compatible with traditional methods and appropriate for a large number of applications.-
Idioma: dc.languageen-
Direitos: dc.rightsCopyright 2011 Permission to copy without fee all or part of the material printed in JIDM is granted provided that the copies are not made or distributed for commercial advantage, and that notice is given that copying is by permission of the Sociedade Brasileira de Computação. Fonte: Informação contida no artigo.-
Palavras-chave: dc.subjectAttribute selection-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectLazy learning-
Título: dc.titleImproving lazy attribute selection.-
Aparece nas coleções:Repositório Institucional - UFOP

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