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Resumen El problema de desbalance en la clasificación se presenta en conjuntos de datos que tienen una cantidad grande de datos de cierto tipo (clase mayoritaria), mientras que el número de datos del tipo contrario es considerablemente menor (clase minoritaria). En este artículo se hace un breve resumen de la teoría de conjuntos aproximados basados en relaciones de similitud para su utilización en la implementación en Weka de tres filtros para tratar el problema de desbalance de clases. Luego se realiza un análisis de los resultados en dos conjuntos de datos para probar su validación, obteniéndose resultados satisfactorios. English abstract The class imbalance problem is shown in datasets which have a great amount of data of a certain type (majority class), whilst in the case of the contrary data type it is considerably less (minority class). In this paper, a brief summary of the rough set theory is made based in similarity relations for its use on three filters Weka for class imbalance management. Finally, an analysis of the results in both sets of data is made in order to prove its validation, obtaining satisfying results.
Imbalance, Clasificación, Conjuntos Aproximados, Desbalance, Filtros, Classification, Rough Sets, Imbalance, Filters, Conjuntos Aproximados, Management. Industrial management, Filters, Filtros, Clasificación, Rough Sets, Classification, HD28-70, Desbalance
Imbalance, Clasificación, Conjuntos Aproximados, Desbalance, Filtros, Classification, Rough Sets, Imbalance, Filters, Conjuntos Aproximados, Management. Industrial management, Filters, Filtros, Clasificación, Rough Sets, Classification, HD28-70, Desbalance
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