Improving Rough Set Rule-Based Classification by Supplementary Rules

Masahiro Inuiguchi; Keisuke Washimi

doi:10.20965/jaciii.2015.p0747

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JACIII Vol.19 No.6 pp. 747-758

doi: 10.20965/jaciii.2015.p0747

(2015)

Paper:

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Improving Rough Set Rule-Based Classification by Supplementary Rules

Masahiro Inuiguchi and Keisuke Washimi

Graduate School of Engineering Science, Osaka University
Toyonaka, Osaka 560-8531, Japan

Received:

May 18, 2015

Accepted:

July 28, 2015

Published:

November 20, 2015

Keywords:

rough set, MLEM2, robustness measure, supplementary rules

Abstract

In rough set approaches, decision rules are induced from a given data set consisting of attribute values and a decision value. Induced rules are used to classify new objects, but this classification is not perfect, perhaps because the given data set does not include all possible patterns. No induced decision rules are matched totally for objects having missing patterns, and partially matched decision rules are used to estimate their classes. The classification accuracy of such an object is usually lower than that of an object totally matching decision rules. To improve the classification accuracy, we propose adding supplementary rules to the induced rules, defining the supplementary rules to improve the classification accuracy of objects only partially matching decision rules. We propose an algorithm for inducing supplementary rules, considering four classifiers consisting of supplementary rules together with originally induced rules.We investigate their performance. We also compare their classification accuracies to that of conventional classifier with originally induced rules.

Cite this article as:

M. Inuiguchi and K. Washimi, “Improving Rough Set Rule-Based Classification by Supplementary Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.6, pp. 747-758, 2015.

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References

[1] Z. Pawlak, “Rough sets,” Int. J. of Computer and Information Sciences, Vol.11, No.5, pp. 341-356, 1982.
[2] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishing, Dordrecht, 1991.
[3] J. W. Grzymala-Busse, “LERS: A System for Learning from Examples Based on Rough Sets,” R. Slowi’nski (Ed.), Intelligent Decision Support: Handbook of Application and Advances of the Rough Set Theory, pp. 3-18, 1992.
[4] S. Tsumoto, “Automated Extraction of Medical Expert System Rules from Clinical Databases Based on Rough Set Theory,” Information Sciences, Vol.112, pp. 67-84, 1998.
[5] L. P. Khoo, S. B. Tor, and L. Y. Zhai, “Rough-Set-Based Approach for Classification and Rule Induction,” Int. J. of Advanced Manufacturing Technology, Vol.15, No.6, pp. 438-444, 1999.
[6] J. G. Bazan, H. S. Nguyen, S. H. Nguyen, P. Synak, and J. Wróblewski, “Rough Set Algorithm in Classification Problem,” S. Tsumoto, et al. (Eds.), Rough Set Methods and Applications, Physica-Verlag, Heidelberg, pp. 49-88, 2000.
[7] J. W. Grzymala-Busse, “MLEM2-Discretization During Rule Induction,” Intelligent Information Processing and WEB Mining Systems, Vol.22, pp. 499-508, 2003.
[8] S. Greco, B. Matarazzo, R. Slowínski, and J. Stefanowski, “An Algorithm for Induction Decision Rules Consistent with the Dominance Principle,” W. Ziarko and Y. Yao, (Eds.) Rough Sets and Current Trends in Computing, 2nd Int. Conf., Revised Papers, LNCS 2005, Springer-Verlag, Berlin, pp. 304-313, 2001.
[9] J. W. Grzymala-Busse, “Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction,” Trans. on Rough Sets, Vol.I, LNCS 3100, pp. 78-95, 2004.
[10] J. W. Grzymala-Busse, J. Stefanowski, and S. Wilk, “A Comparison of Two Approaches to Data Mining from Imbalanced Data,” J. of Intelligent Manufacturing, Vol.16, No.6, pp. 565-573, 2005.
[11] Y. Kusunoki, M. Inuiguchi, and J. Stefanowski, “Rule Induction via Clustering Decision Classes,” Int. J. of Innovative Computing, Information and Control, Vol.4, No.10, pp. 2663-2677, 2008.
[12] J. Blaszczynski, R. Slowi’nski, and M. Szleg, “Sequential Covering Rule Induction Algorithm for Variable Consistency Rough Set Approaches,” Information Sciences, Vol.181, No.5, pp. 987-1002, 2011.
[13] M. Ohki and M. Inuiguchi, “Robustness Measure of Decision Rules,” P. Lingras, M. Wolski, C. Cornelis, S. Mitra, and P. Wasilewski (Eds.), Proc. of the 8th Int. Conf. on Rough Sets and Knowledge Technology, LNAI 8171, Springer, pp. 166-177, 2013.
[14] M. Inuiguchi and T. Hamakawa, “The Utilities of Imprecise Rules and Redundant Rules for Classifiers,” V.-N. Huynh, T. Denoeux, D. H. Tran, A. C. Le, and S. B. Pham (Eds.), Knowledge and Systems Engineering: Proc. of the 5th Int. Conf. KSE 2013, Vol.2, AISC 245, Springer, pp. 45-56, 2013.

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 Sciences, Vol.11, No.5, pp. 341-356, 1982.

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

[3] [3] J. W. Grzymala-Busse, “LERS: A System for Learning from Examples Based on Rough Sets,” R. Slowi’nski (Ed.), Intelligent Decision Support: Handbook of Application and Advances of the Rough Set Theory, pp. 3-18, 1992.

[4] [4] S. Tsumoto, “Automated Extraction of Medical Expert System Rules from Clinical Databases Based on Rough Set Theory,” Information Sciences, Vol.112, pp. 67-84, 1998.

[5] [5] L. P. Khoo, S. B. Tor, and L. Y. Zhai, “Rough-Set-Based Approach for Classification and Rule Induction,” Int. J. of Advanced Manufacturing Technology, Vol.15, No.6, pp. 438-444, 1999.

[6] [6] J. G. Bazan, H. S. Nguyen, S. H. Nguyen, P. Synak, and J. Wróblewski, “Rough Set Algorithm in Classification Problem,” S. Tsumoto, et al. (Eds.), Rough Set Methods and Applications, Physica-Verlag, Heidelberg, pp. 49-88, 2000.

[7] [7] J. W. Grzymala-Busse, “MLEM2-Discretization During Rule Induction,” Intelligent Information Processing and WEB Mining Systems, Vol.22, pp. 499-508, 2003.

[8] [8] S. Greco, B. Matarazzo, R. Slowínski, and J. Stefanowski, “An Algorithm for Induction Decision Rules Consistent with the Dominance Principle,” W. Ziarko and Y. Yao, (Eds.) Rough Sets and Current Trends in Computing, 2nd Int. Conf., Revised Papers, LNCS 2005, Springer-Verlag, Berlin, pp. 304-313, 2001.

[9] [9] J. W. Grzymala-Busse, “Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction,” Trans. on Rough Sets, Vol.I, LNCS 3100, pp. 78-95, 2004.

[10] [10] J. W. Grzymala-Busse, J. Stefanowski, and S. Wilk, “A Comparison of Two Approaches to Data Mining from Imbalanced Data,” J. of Intelligent Manufacturing, Vol.16, No.6, pp. 565-573, 2005.

[11] [11] Y. Kusunoki, M. Inuiguchi, and J. Stefanowski, “Rule Induction via Clustering Decision Classes,” Int. J. of Innovative Computing, Information and Control, Vol.4, No.10, pp. 2663-2677, 2008.

[12] [12] J. Blaszczynski, R. Slowi’nski, and M. Szleg, “Sequential Covering Rule Induction Algorithm for Variable Consistency Rough Set Approaches,” Information Sciences, Vol.181, No.5, pp. 987-1002, 2011.

[13] [13] M. Ohki and M. Inuiguchi, “Robustness Measure of Decision Rules,” P. Lingras, M. Wolski, C. Cornelis, S. Mitra, and P. Wasilewski (Eds.), Proc. of the 8th Int. Conf. on Rough Sets and Knowledge Technology, LNAI 8171, Springer, pp. 166-177, 2013.

[14] [14] M. Inuiguchi and T. Hamakawa, “The Utilities of Imprecise Rules and Redundant Rules for Classifiers,” V.-N. Huynh, T. Denoeux, D. H. Tran, A. C. Le, and S. B. Pham (Eds.), Knowledge and Systems Engineering: Proc. of the 5th Int. Conf. KSE 2013, Vol.2, AISC 245, Springer, pp. 45-56, 2013.