Abstract
Determining the correct threshold values for probabilistic rough set models has been a heated issue among the community. This article will formulate a game-theoretic approach to calculating these thresholds to ensure correct approximation region size. By finding equilibrium within payoff tables created from approximation measures and modified conditional risk strategies, we provide the user with tolerance levels for their loss functions. Using the tolerance values, new thresholds are calculated to provide correct classification regions. Better informed decisions can be made when utilizing these tolerance values. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Herbert, J. P., & Yao, J. (2008). Game-theoretic risk analysis in decision-theoretic rough sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5009 LNAI, pp. 132–139). https://doi.org/10.1007/978-3-540-79721-0_22
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