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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Learning Optimal Parameters in Decision-Theoretic Rough Sets
verfasst von : Joseph P. Herbert, JingTao Yao
Erschienen in: Rough Sets and Knowledge Technology
Verlag: Springer Berlin Heidelberg
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A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a probabilistic decision space. New values for loss functions are learned from a sequence of risk modifications derived from game-theoretic analysis of the relationship between two classification measures. Using game theory to maximize these measures results in a learning method to reformulate the loss functions. The decision-theoretic rough set model acquires initial values for these parameters through a combination of loss functions provided by the user. The new game-theoretic learning method modifies these loss functions according to an acceptable threshold.