A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

Yasuo Kudo; Tetsuya Murai

doi:10.20965/jaciii.2013.p0371

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JACIII Vol.17 No.3 pp. 371-376

doi: 10.20965/jaciii.2013.p0371

(2013)

Paper:

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A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

Yasuo Kudo^* and Tetsuya Murai^**

^*College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan

^**Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan

Received:

December 8, 2012

Accepted:

January 20, 2013

Published:

May 20, 2013

Keywords:

rough set, attribute reduction, parallel computation, open multiprocessing

Abstract

In this paper, we propose a parallel computation framework for a heuristic attribute reduction method. Attribute reduction is a key technique to use rough set theory as a tool in data mining. The authors have previously proposed a heuristic attribute reduction method to compute as many relative reducts as possible from a given dataset with numerous attributes. We parallelize our method by using open multiprocessing. We also evaluate the performance of a parallelized attribute reduction method by experiments.

Cite this article as:

Y. Kudo and T. Murai, “A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.3, pp. 371-376, 2013.

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References

[1] Z. Pawlak, “Rough Sets,” Int. J. of Computer and Information Science, Vol.11, pp. 341-356, 1982.
[2] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publisher, 1991.
[3] A. Chouchoulas and A. Shen, “Rough set-aided keyword reduction for text categorization,” Applied Artificial Intelligence, Vol.15, No.9, pp. 843-873, 2001.
[4] K. Hu, L. Diao, Y. Lu, and C. Shi, “A heuristic optimal reduct algorithm,” Proc. of IDEAL2000, LNSC, Vol.1983, pp. 139-144, Springer, 2000.
[5] M. Kryszkiewicz and P. Lasek, “FUN: Fast discovery of minimal sets of attributes functionally determining a decision attribute,” Trans. on Rough Sets IX, LNCS, Vol.5390, pp. 76-95, Springer, 2008.
[6] Y. Kudo and T. Murai, “Heuristic algorithm for attribute reduction based on classification ability by condition attributes,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.1, pp. 102-109, 2011.
[7] J. Zhang, J.Wang, D. Li, H. He, and J. Sun, “A new heuristic reduct algorithm based on rough sets theory,” Proc. ofWAIM2003, LNCS, Vol.2762, pp. 247-253, Springer, 2003.
[8] Y. Kudo and T. Murai, “An attribute reduction algorithm by switching exhaustive and heuristic computation of relative reducts,” Proc. of IEEE GrC2010, pp. 265-270, IEEE, 2010.
[9] Y. Kudo and T.Murai, “A Parallel Computation Method of Attribute Reduction,” Proc. of ISCIIA 2012, 2012.
[10] L. Polkowski, “Rough Sets: Mathematical Foundations,” Advances in Soft Computing, Physica-Verlag, 2002.
[11] A. Skowron and C.M. Rauszer, “The discernibility matrix and functionsin information systems,” R. Słowiński (Ed.), Intelligent Decision Support: Handbook of Application and Advance of the Rough Set Theory, Kluwer Academic Publishers, pp. 331-362, 1992.
[12] OpenMP
http://openmp.org/wp/
[13] OpenMP from Wikipedia
http://en.wikipedia.org/wiki/OpenMP/
[14] UCI Machine Learning Repository
http://archive.ics.uci.edu/ml/

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Z. Pawlak, “Rough Sets,” Int. J. of Computer and Information Science, Vol.11, pp. 341-356, 1982.

[2] [2] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publisher, 1991.

[3] [3] A. Chouchoulas and A. Shen, “Rough set-aided keyword reduction for text categorization,” Applied Artificial Intelligence, Vol.15, No.9, pp. 843-873, 2001.

[4] [4] K. Hu, L. Diao, Y. Lu, and C. Shi, “A heuristic optimal reduct algorithm,” Proc. of IDEAL2000, LNSC, Vol.1983, pp. 139-144, Springer, 2000.

[5] [5] M. Kryszkiewicz and P. Lasek, “FUN: Fast discovery of minimal sets of attributes functionally determining a decision attribute,” Trans. on Rough Sets IX, LNCS, Vol.5390, pp. 76-95, Springer, 2008.

[6] [6] Y. Kudo and T. Murai, “Heuristic algorithm for attribute reduction based on classification ability by condition attributes,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.1, pp. 102-109, 2011.

[7] [7] J. Zhang, J.Wang, D. Li, H. He, and J. Sun, “A new heuristic reduct algorithm based on rough sets theory,” Proc. ofWAIM2003, LNCS, Vol.2762, pp. 247-253, Springer, 2003.

[8] [8] Y. Kudo and T. Murai, “An attribute reduction algorithm by switching exhaustive and heuristic computation of relative reducts,” Proc. of IEEE GrC2010, pp. 265-270, IEEE, 2010.

[9] [9] Y. Kudo and T.Murai, “A Parallel Computation Method of Attribute Reduction,” Proc. of ISCIIA 2012, 2012.

[10] [10] L. Polkowski, “Rough Sets: Mathematical Foundations,” Advances in Soft Computing, Physica-Verlag, 2002.

[11] [11] A. Skowron and C.M. Rauszer, “The discernibility matrix and functionsin information systems,” R. Słowiński (Ed.), Intelligent Decision Support: Handbook of Application and Advance of the Rough Set Theory, Kluwer Academic Publishers, pp. 331-362, 1992.

[12] [12] OpenMP
http://openmp.org/wp/

[13] [13] OpenMP from Wikipedia
http://en.wikipedia.org/wiki/OpenMP/

[14] [14] UCI Machine Learning Repository
http://archive.ics.uci.edu/ml/

A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

Yasuo Kudo* and Tetsuya Murai**

Yasuo Kudo^* and Tetsuya Murai^**