Journal of the Ergonomics Society of Korea (대한인간공학회지)

Ergonomics Society of Korea (대한인간공학회)
권호 표지
DOI QR코드

DOI QR Code

Knowledge Extraction from Affective Data using Rough Sets Model and Comparison between Rough Sets Theory and Statistical Method

러프집합이론을 중심으로 한 감성 지식 추출 및 통계분석과의 비교 연구

  • Hong, Seung-Woo (Department of Information Management Engineering, Korea University) ;
  • Park, Jae-Kyu (Department of Information Management Engineering, Korea University) ;
  • Park, Sung-Joon (Department of Industrial and Management Engineering, Namseoul University) ;
  • Jung, Eui-S. (Department of Information Management Engineering, Korea University)
  • 홍승우 (고려대학교 정보경영공학과) ;
  • 박재규 (고려대학교 정보경영공학과) ;
  • 박성준 (남서울대학교 산업경영공학과) ;
  • 정의승 (고려대학교 정보경영공학과)
  • Received : 2010.06.07
  • Accepted : 2010.07.09
  • Published : 2010.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

Keywords

References

  1. 변증남, 방원철, 러프집합의 이론과 응용, 청문각, 2006.
  2. 손창식, 정환묵, 퍼지추론에서 러프집합을 이용한 감성 데이터의 분류, Proceedings of KFIS Fall Conference, 14(2), 2004.
  3. 이동훈, 김대욱, 심귀보, MLP에 기반한 감성인식 모델개발, Proceedings of KFIS Spring Conference, 16(1), 2006.
  4. 서완석, 김재련, 러프집합이론과 SOM을 이용한 연속형 속성의 이산화, Journal of the Society of Korea Industrial and Systems Engineering, 28(1), 2005.
  5. 성덕현, 정의승, 조용주, 한국인의 의복 제작을 위한 체형별 사이징 체계 개발, Journal of the Ergonomics Society of Korea, 24(4), 31-37, 2005. https://doi.org/10.5143/JESK.2005.24.4.031
  6. 유재진, 김재련, 러프집합을 이용한 의사결정나무의 노드 선택 방법, 한국산업경영시스템학회 2003 추계학술대회 논문집, 2003.
  7. Nagamachi, M., Okazaki, Y. and Ishikawa M., Kansei engineering and application of the rough sets model, Proc. IMechE Special Issue Paper, 220(1), 2006.
  8. Nagamachi, M., Perspectives and new trend of Kansei/Affective Engineering, TQM Journal, 2008.
  9. Pawlak, Z., Rough sets: theoretical aspects of reasoning about data, Kluwer Academy Publisher, 1991.
  10. Yan, H-B., Huynh, V-N., Murai, T. and Nakamori, Y., Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis, Information Sciences, 178, 4080-4093, 2008. https://doi.org/10.1016/j.ins.2008.06.023
  11. Zhai, L-Y., Khoo, L-P. and Zhong, Z-W., A rough set based decision support approach to improving consumer affective satisfaction in product design, International Journal of Industrial Ergonomics, 39, 295-302, 2009. https://doi.org/10.1016/j.ergon.2008.11.003