JACIII Vol.22 No.3 pp. 404-410
doi: 10.20965/jaciii.2018.p0404


Knowledge Acquisition from Rough Sets Using Merged Decision Rules

Yoshiyuki Matsumoto* and Junzo Watada**

*Shimonoseki City University
2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

** Petronas University of Technology
32610 Seri Iskander, Perak Darul Ridzuan, Malaysia

December 15, 2017
April 3, 2018
May 20, 2018
rough sets, decision rules, time-series data, knowledge acquisition

Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yen–dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules.

  1. [1] Y. Matsumoto and J. Watada, “Improvement of Chaotic Short-term Forecasting on Fuzzy Reasoning and Tuning on Genetic Algorithm,” J. of Japan Society of Fuzzy Theory and Intelligent Informatics, Vol.16, No.1, pp. 44-52, 2004.
  2. [2] Z. Pawlak, “Rough Sets,” Int. J. of Computer and Information Science, Vol.11, No.5, pp. 341-356, 1982.
  3. [3] Z. Pawlak, “Rough Sets – Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, 1991.
  4. [4] C. Goh and R. Law, “Incorporation the rough sets theory,” Chemometrics and Intelligent Laboratory Systems, Vol.47, No.1, pp. 1-16, 2003.
  5. [5] B. Predki et al., “ROSE-Software Implementation of the rough set theory,” Rough Sets and Current Trends in Computing, Springer, pp. 605-608, 1998.
  6. [6] B. Predki and S. Wilk, “Rough Set Based Data Exploration Using ROSE System,” Foundations of Intelligent Systems, Springer, pp. 172-180, 1999.
  7. [7] Z. Pawlak, “Rough classification,” Int. J. Human-Computer Studies, Vol.51, No.15, pp. 369-383, 1999.
  8. [8] N. Mori, H. Tanaka, and K. Inoue, “Rough sets and Kansei: Knowledge acquisition and reasoning from Kansei data,” Kaibundo Publishing, 2004.
  9. [9] S. Tan, X. Cheng, and H. Xu, “An efficient global optimization approach for rough set based dimensionality reduction,” Int. J. of Innovative Computing, Information and Control, Vol.3, No.3, pp. 725-736, 2007.
  10. [10] S. Greco, B. Matarazzo, and R. Slowinski, “Rough sets theory for multi-criteria decision analysis,” European J. of Operational Research, Vol.129, No.1, pp. 1-47, 2001.
  11. [11] H. Tanaka and S. Tsumoto, “Rough sets and Expert System,” Mathematical Sciences, pp. 76-83, 1994.
  12. [12] T. Harada and R. Tanaka, “Analysis of Specifications for Web Screen-Design Using Rough Sets,” J. of Adv. Comput. Intell. Intell. Inform., Vol.10, No.5, pp. 688-694 , 2006
  13. [13] D. Kim and S. Y. Bang , “IRIS Data Classification Using Tolerant Rough Sets,” J. of Adv. Comput. Intell. Intell. Inform., Vol.4, No.5, 2000.
  14. [14] K. Gronhaug and M. C. Gilly, “A transaction cost approach to consumer dissatisfaction and complaint action,” J. of Economic Psychology, Vol.12, No.1, pp. 165-183, 1991.
  15. [15] C. Lin, J. Watada, and G. Tzeng, “Rough sets theory and its application to management engineering,” Proc. of Int. Symp. of Management Engineering, pp. 170-176, 2008.
  16. [16] R. Azibi and D. Vanderpooten, “Construction of rule-based assignment models,” European J. of Operational Research, Vol.138, No.2, pp. 274-293, 2002.
  17. [17] R. Li and Z. O.Wang, “Mining classification rules using rough set and neural networks,” European J. of Operational Research, Vol.157, No.2, pp. 439-448, 2004.
  18. [18] B. Walczak and D. L. Massart, “Rough set theory,” Chemometrics and Intelligent Laboratory, Vol.47, No.1, pp. 1-16, 1999.
  19. [19] M. Quafafou, “α-RST: a generalization of rough set theory,” Information Sciences, Vol.124, No.4, pp. 301-316, 2000.
  20. [20] Y. Jhieh, G. Tzeng, and F. Wang, “Rough set Theory in Analyzing the Attributes of Combination Values for insurance market,” Expert System with Applications, Vol.32, No.1, 2007.
  21. [21] M. J. Beynon and M. J. Peel, “Variable precision rough set theory and data discrimination: an application to corporate failure prediction, Omega,” Vol.29, No.6, pp. 561-576, 2001.
Cite this article as:
Yoshiyuki Matsumoto and Junzo Watada, “Knowledge Acquisition from Rough Sets Using Merged Decision Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.3, pp. 404-410, 2018
Yoshiyuki Matsumoto and Junzo Watada, J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.3, pp. 404-410, 2018

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Last updated on Jun. 22, 2018