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RSKT
2009
Springer

Learning Optimal Parameters in Decision-Theoretic Rough Sets

9 years 10 months ago
Learning Optimal Parameters in Decision-Theoretic Rough Sets
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.
Joseph P. Herbert, Jingtao Yao
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where RSKT
Authors Joseph P. Herbert, Jingtao Yao
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