Selección de Atributos y Casos para la Clasificación de Bancos de Datos Sociales (Attributes and Cases Selection for Social Data Classification)

Yenny Villuendas (, Cornelio Yáñez (, Carmen Rey (

1CIDETEC del Instituto Politécnico Nacional
2CIC del Instituto Politécnico Nacional
3CEE de la Universidad de Ciego de Ávila

This paper appears in: Revista IEEE América Latina

Publication Date: Oct. 2015
Volume: 13,   Issue: 10 
ISSN: 1548-0992

The current paper presents an effective method to improve the classification of social data, by selecting relevant cases (objects) and attributes (features). This is accomplished using a hybrid approach that combines metaheuristic algorithms and Rough Set Theory. When selecting some relevant attributes and cases of the training data of the Nearest Neighbor classifier, this model has been found to be more efficient in the correct discrimination of objects. Experimental results show that applying hybrid algorithms for training set preprocessing contributes to increment the desired efficiency and robustness of the classifier model over social data.

Index Terms:
pattern classification, metaheuristic algorithms, data preprocessing, social data   

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