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JACIII Vol.10 No.5 pp. 666-672
doi: 10.20965/jaciii.2006.p0666
(2006)

Paper:

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Reduction of Attribute Values for Kansei Representation

Yuji Muto, Mineichi Kudo, and Tetsuya Murai

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

Received:
December 29, 2005
Accepted:
April 10, 2006
Published:
September 20, 2006
Creative Commons License
Keywords:
granular computing, discernibility, reduction, attribute value, Kansei analysis
Abstract
In this paper, we discuss attribute-value reduction for Kansei analysis using information granulation. In the traditional “reduction” sense, the goal is to find the smallest number of attributes enabling us to discern each tuple or each decision class. Once we focus on the number of attribute values, i.e., the size and/or resolution of each attribute domain, another criterion is needed. We must ask ourselves which is better: 1) discerning them with a single attribute described in detail, or 2) discerning them with a few attributes described roughly. Our study answers this question. If we evaluate this difference in the light of understandability or of Kansei representation, we may prefer working with a few attributes described roughly because they yield simpler descriptions. To do this, we propose a criterion and an algorithm to find near-optimal solutions for the criterion, detailing results for databases in the UCI Machine Learning Repository.
Cite this article as:
Y. Muto, M. Kudo, and T. Murai, “Reduction of Attribute Values for Kansei Representation,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.5, pp. 666-672, 2006.
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References
  1. [1] L. A. Zadeh, “Fuzzy Sets and Information Granularity,” Advances in Fuzzy Set Theory and Applications, pp. 3-18, 1979.
  2. [2] T. Y. Lin, “Granular Computing on Binary Relation I: Data Mining and Neighborhood Systems, II: Rough Set Representations and Belief Functions,” In L. Polkowski, and A. Skowron (eds.), Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, pp. 107-121, pp. 122-140, 1998.
  3. [3] A. Skowron and J. Stepaniuk, “Information Granules: Towards Foundations of Granular Computing,” International Journal of Intelligent Systems, Vol.16, pp. 57-85, 2001.
  4. [4] M. Nagamachi, “Kansei Engineering: A new ergonomic consumeroriented technology for product development,” International Journal of Industrial Ergonomics, Vol.15, pp. 3-11, 1995.
  5. [5] Y. Muto and M. Kudo, “Discernibility-Based Variable Granularity and Kansei Representations,” Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Lecture Notes in Artificial Intelligence, Vol.3641, pp. 692-700, 2005.
  6. [6] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991.
  7. [7] Z. Pawlak, “Rough classification,” International Journal of Human-Computer Studies, Vol.51, pp. 369-383, 1999.
  8. [8] W. Ziarko, “Variable Precision Rough Set Model,” Journal of Computer and System Sciences, Vol.46, pp. 39-59, 1993.
  9. [9] C. L. Blake and C. J. Merz, UCI Repository of machine learning databases,
    http://www.ics.uci.edu/˜mlearn/MLRepository.html .

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