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Attribute Reduction using Forward Selection and Relative Reduct Algorithm

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International Journal of Computer Applications
© 2010 by IJCA Journal
Number 3 - Article 3
Year of Publication: 2010
Authors:
P.Kalyani
Dr. M.Karnan
10.5120/1564-1499

P.Kalyani and Dr. M.Karnan. Article:Attribute Reduction using Forward Selection and Relative Reduct Algorithm. International Journal of Computer Applications 11(3):8–12, December 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {P.Kalyani and Dr. M.Karnan},
	title = {Article:Attribute Reduction using Forward Selection and Relative Reduct Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {3},
	pages = {8--12},
	month = {December},
	note = {Published By Foundation of Computer Science}
}

Abstract

Attribute reduction of an information system is a key problem in rough set theory and its applications. Rough set theory has been one of the most successful methods used for feature selection. Rough set is one of the most useful data mining techniques. This paper proposes relative reduct to solve the attribute reduction problem in roughest theory. It is the most promising technique in the Rough set theory, a new mathematical approach to reduct car dataset using relative reduct algorithm. The redundant attributes are eliminated in order to generate the effective reduct set (i.e., reduced set of necessary attributes) or to construct the core of the attribute set. The technique was originally proposed to avoid the calculation of discernibility functions or positive regions, which can be computationally expensive without optimizations. This paper analyses the efficiency of the proposed backward relative reduct algorithm against forward selection algorithm. The experiments are carried out on car data base of UCI machine learning repository.

Reference

  • Z. Pawlak (1982) Rough sets. International Journal of Computer and Information Sciences, vol.11, pp. 341–356.
  • Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing, Dordrecht, 1991.
  • P. J. Lingras, “Data mining using extensions of the rough set model,” J.Amer. Soc. Inf. Sci.,vol. 49, pp. 415–422, May 1998.
  • N.O. Attoh-Okine, “Rough set application to data mining principles in pavement management database,” J. Comput. Civil Eng., vol. 11, pp.231–237, Apr. 1997.
  • Z. Pawlak, “Rough set approach to knowledge-based decision support,” Eur. J. Oper. Res.,vol. 99, pp. 48–75, Jan. 1997.
  • Liu Qin. Rough Set and Rough Reasoning. Beijing: Science Press, 2001
  • L. K. Terje, “Rough modeling—Extracting compact models from large databases,” Mastersthesis, Knowledge Systems Group, Norwegian Univ. Sci. Technol., Trondheim, Norway,1999.
  • Chouchoulas, J. Halliwell and Q. Shen. On the Implementation of Rough Set Attribute Reduction.Proceedings of the 2002 UK Workshop on Computational Intelligence, pp. 18-23. 2002.
  • J.J. Alpigini, J.F. Peters, J. Skowronek,N. Zhong (Eds.): Rough Sets and Current Trends in Computing, Third International Conference, RSCTC 2002, Malvern,PA, USA, October 14-16, 2002, Proceedings. Lecture Notes in Computer Science 2475 Springer 2002, ISBN 3-540-44274-X.
  • R. Jensen, Q. Shen (2004) Semantics-preserving dimensionality reduction: Rough and fuzzy rough based approaches. IEEE Transactions on Knowledge and Data Engineering, vol. 16, pp.1457–1471.
  • Blake,C.L. and C.J.Merz, 1998. UCI Repository of machine learning databases. Irvine, University of California, http://www.ics.uci.edu/~mlear n/.