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구간 데이터를 위한 가변정밀도 러프집합 모형

A Variable Precision Rough Set Model for Interval data

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참고문헌

키워드

주제분류산업공학

초록

Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However,there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable precision rough set model has been proposed. In this paper, we propose a variable precision rough set model for interval values in which classification errors are allowed in determining if two intervals are same. To build the model, we define equivalence class, upper approximation, lower approximation, and boundary region. Then, we check if each of 11 characteristics on approximation that works in Pawlak’s rough set model is valid for the proposed model or not.

Abstract

Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However,there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable precision rough set model has been proposed. In this paper, we propose a variable precision rough set model for interval values in which classification errors are allowed in determining if two intervals are same. To build the model, we define equivalence class, upper approximation, lower approximation, and boundary region. Then, we check if each of 11 characteristics on approximation that works in Pawlak’s rough set model is valid for the proposed model or not.

참고문헌 (11)

  1. Grzymala-Busse,J.W, "Data mining based on rough sets, In Data Mining : Opportunities and Challenges", pp.142~173 (2003), Idea Group Publishing
  2. Huang,K.Y, "Application of VPRS Model with Enhanced Threshold Parameter Selection Mechanism to Automatic Stock Market Forecasting and Portfolio Selection", Expert Systems with Applications, 36(9), pp.11652~11661 (2009)
  3. Li,W.H, "A Variable Precision Rough Set Based Modeling Method for Pulsed GTAW", International Journal of Advanced Manufacturing Technology, 36(11), pp.1072~1079 (2008)
  4. Ningler,M, "Adapted Variable Precision Rough Set Approach for EEG Analysis", Artificial Intelligence in Medicine, 47(3), pp.239~261 (2009)
  5. Pan,X, "A Variable Precision Rough Set Approach to the Remote Sensing and Use", Cover Classification, 2010, pp.36(12), 1466~1473
  6. Pawlak,Z, "Rough sets", International Journal of Information Science, 11(5), pp.341~356 (1982)
  7. Xie,G, "Using VPRS to Mine the Significance of Risk Factors in IT Project Management", Proceedings of Rough Sets and Knowledge technology,Lecture Notes in Artificial Intelligence, 통권 : 4062, pp.750~757 (2006), Springer Verlag
  8. Xie,G, "Variable Precision Rough Set for Group Decision-Making:An Application", International Journal of Approximate Reasoning, 49(2), pp.331~343 (2008)
  9. Xie,G, "Dynamic Risk Management in Petroleum Project Investment Based on a Variable Precision Rough Set Model", Technological Forecasting and Social Change, 77(6), pp.891~901 (2010)
  10. Zhao,W.Q, "Classifying email Using Variable Precision Rough Set Approach", Proceedings of Rough Sets and Knowledge technology,Lecture Notes in Artificial Intelligence, 통권 : 4062, pp.766~771 (2006), Springer Verlag
  11. Ziarko,W, "Variable precision rough set model", Journal of Computer and System Science, 46, pp.39~56 (1993)

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