Advertisement
Published in:
2011 | OriginalPaper | Chapter
An Optimization Viewpoint of Decision-Theoretic Rough Set Model
Authors : Xiuyi Jia, Weiwei Li, Lin Shang, Jiajun Chen
Published in: Rough Sets and Knowledge Technology
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This paper considers an optimization viewpoint of decision-theoretic 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.