A new algorithm for feature selection based on rough sets theory

Authors

  • 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

Keywords:

Feature selection, reduct, rough sets

Abstract


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

References

N. Zhong, J. Dong, S. Ohsuga “Using Rough sets with heuristics for feature selection” Journal of Intelligent Information Systems. Vol. 16. 2001. pp. 199-214.

J. Wroblewski. “Finding minimal reducts using genetic algorithms”. En: Wang, P.P. (Ed). Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences, North Carolina, USA. 1995. pp. 186-189.

J.S Deogun. “Feature selection and effective classifiers”. Journal of ASIS. Vol 49. 1998. pp. 423-434.

S.K. Choubey. “A comparison of feature selection algorithms in the context of rough classifiers”. Proceedings of Fifth IEEE International Conference on Fuzzy Systems. Vol. 2. 1996. pp. 1122-1128.

R. Kohavi, B. Frasca. “Useful feature subsets and Rough set Reducts”. Proceedings of the Third International Workshop on Rough Sets and Soft Computing. San José, California. 1994. pp. 310-317.

H. Liu, H. Motoda. “Feature Selection Boston, MA : Kluwer academic Publishers”. En: http://citeseer.ist.psu.edu/321378.html 1998. Consultado el 7 de mayo de 2006.

R. Jensen, S. Qiang. “Finding rough sets reducts with Ant colony optimization”. http://www.inf.ed.ac.uk/publications/online/0201.pdf. 2003. Consultado el 5 de noviembre de 2005.

Z. Pawlak. “Rough sets”. International Journal of Information & Computer Sciences. Vol. 11. 1982. pp. 341-356.

J. Komorowski, Z. Pawlak, Z. “Rough Sets: A tutorial. In Pal, S.K. and Skowron, A. (Eds.) Rough Fuzzy Hy-bridization: A new trend in decision-making.” Springer. 1999. pp. 3-98.

I. Dunstsh, Ivo, G. Gunter. “Rough set data analysis”. En: http://citeseer.nj.nec.com/dntsch00rough.html. 2000. Consultado el 20 de mayo de 2006.

Z. Pawlak, “Rough Sets Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht”. 1991. En: http://citeseer.ist.psu.edu/con-text/36378.html. Consultado el 7 de abril de 2006.

R. Bello, Z. Valdivia, M.M. García, L. Reynoso. Aplicaciones de la inteligencia artificial. México. 1ª. ed. Guadalajara. Universidad de Guadalajara. 2002. pp. 62-64

R. Bello, A. Nowe, A. Puris. “Two Step Ant Colony Sys-tem to Solve the Feature Selection Problem”. Lectures Notes on Computer Sciences. Springer-Verlag. 2006. pp. 588-596.

B.S. Ahn. “The integrated methodology of rough set theory and artificial neural networks for business failure predictions”. Expert Systems with Applications. Vol. 18. 2000. pp. 65-74.

A. Ohrn. “Rosetta Technical Reference Manual. Department of Computer and Information Science” Norwegian University of Science and Technology. Noruega, 2002.

Y. S. Wong, C. J. Butz. “Methodologies for Knowledge Discovery and Data Mining”. Zhong y Zhou (Eds.) On Information-Theoretic Measures of attribute importance. pp. 231-238. En: http://citeseer.ist.psu. edu/yao99informationtheoretic.html. Consultado el 15 de febrero de 2006.

P. Piñero, L. Arco, M.M. García, Y. Caballero.“Two New Metrics for Feature Selection in Pattern Recognition”, Lectures Notes in Computer Science (LNCS 2905) Springer Verlag, Berlin Heidelberg. 2003. pp. 488-497

T. Mitchell, M. Hill. “Machine Learning”. 1997. http://www.cs.cmu.edu/~tom/mlbook.html. Consultado el 14 de enero de 2006.

C. L Blake, C. J. Merz. “UCI Repository of machine learning databases”. University of California, Irvine, 1998 http://www.ics.uci.edu/~mlearn/. Consultado el 10 de mayo de 2005.

D. Álvarez. Feature selection for data analysis using Rough Sets Theory. Master Thesis of Computer Science Engineering. University of Camagüey. Cuba. 2005. pp. 38-66.

Published

2014-03-31

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