A new algorithm for feature selection based on rough sets theory


  • Yailé Caballero Universidad de Camagüey
  • Delia Álvarez Universidad de Camagüey
  • Analay Baltá Universidad de Camagüey
  • Rafael Bello Universidad Central de Las Villas
  • María García Universidad Central de Las Villas


Feature selection, reduct, rough sets


Rough Sets Theory has opened new trends for the development of data analysis techniques. In this theory, the notion of reduct is very significant, but obtaining a reduct in a decision system is an expensive computing process although very important in data analysis and new discoveries. Because of this, it has been necessary to develop different variants to calculate reducts. The present work looks into the utility that offers Rough Sets in feature selection and a new method is presented with the purpose of calculate a good reduct. This new method consists of a greedy algorithm that uses heuristics to work out a good reduct in acceptable times. Experimental results obtained by using different data sets are presented.

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Author Biographies

Yailé Caballero, Universidad de Camagüey

Departamento de Computación

Delia Álvarez, Universidad de Camagüey

Departamento de Computación

Analay Baltá, Universidad de Camagüey

Departamento de Computación

Rafael Bello, Universidad Central de Las Villas

Departamento de Ciencia de la Computación

María García, Universidad Central de Las Villas

Departamento de Ciencia de la Computación


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How to Cite

Caballero, Y., Álvarez , D., Baltá, A., Bello, R., & García, M. . (2014). A new algorithm for feature selection based on rough sets theory. Revista Facultad De Ingeniería Universidad De Antioquia, (41), 132–144. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/19021