SciELO - Scientific Electronic Library Online

 
vol.18 issue2Feature Selection for Microarray Gene Expression Data Using Simulated Annealing Guided by the Multivariate Joint EntropyEfficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Abstract

VILLUENDAS-REY, Yenny  and  GARCIA-LORENZO, Maria Matilde. Attribute and Case Selection for NN Classifier through Rough Sets and Naturally Inspired Algorithms. Comp. y Sist. [online]. 2014, vol.18, n.2, pp.295-311. ISSN 1405-5546.  http://dx.doi.org/10.13053/CyS-18-2-2014-033.

Supervised classification is one of the most active research fields in the Artificial Intelligence community. Nearest Neighbor (NN) is one of the simplest and most consistently accurate approaches to supervised classification. The training set preprocessing is essential for obtaining high quality classification results. This paper introduces an attribute and case selection algorithm using a hybrid Rough Set Theory and naturally inspired approach to improve the NN performance. The proposed algorithm deals with mixed and incomplete, as well as imbalanced datasets. Its performance was tested over repository databases, showing high classification accuracy while keeping few cases and attributes.

Keywords : Nearest neighbor; case selection; attribute selection.

        · abstract in Spanish     · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License