An optimization viewpoint of decision-theoretic rough set model

  • Authors:

  • Xiuyi Jia;Weiwei Li;Lin Shang;Jiajun Chen

  • Affiliations:

  • School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China and State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

  • Venue:
  • RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
  • Year:
  • 2011

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Abstract

This paper considers an optimization viewpoint of decisiontheoretic rough set model. An optimization problem is proposed by considering the minimization of the decision cost. Based on the optimization problem, cost functions and thresholds used in decision-theoretic rough set model can be learned from the given data automatically. An adaptive learning algorithm Alcofa is proposed. Another significant inference drawn from the solution of the optimization problem is a minimum cost based attribute reduction. The attribute reduction can be interpreted as finding the minimal attribute set to make the decision cost minimum. The optimization viewpoint can bring some new insights into the research on decision-theoretic rough set model.