Abstract
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.
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Jia, X., Li, W., Shang, L., Chen, J. (2011). An Optimization Viewpoint of Decision-Theoretic Rough Set Model. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_60
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DOI: https://doi.org/10.1007/978-3-642-24425-4_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
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