Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/4385
Title: Improving lazy attribute selection.
Authors: Pereira, Rafael Barros
Plastino, Alexandre
Zadrozny, Bianca
Merschmann, Luiz Henrique de Campos
Freitas, Alex Alves
Keywords: Attribute selection
Classification
Lazy learning
Issue Date: 2011
Citation: PEREIRA, R. B. et al. Improving lazy attribute selection. Journal of Information and Data Management - JIDM, v. 2, n. 3, p. 447-462, out. 2011. Disponível em: <https://seer.lcc.ufmg.br/index.php/jidm/article/view/156/95>. Acesso em: 23 jan. 2015.
Abstract: Attribute 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.
URI: http://www.repositorio.ufop.br/handle/123456789/4385
ISSN: 2178-7107
metadata.dc.rights.license: 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.
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