Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets

• Determining probabilistic rough set regions uncertainty levels.
• A heated issue in the community is the determination of optimal values for these thresholds.
• Exploring thresholds and their impacts on uncertainty levels of different regions.
• Using a game to tune the parameters for effective threshold values.
• Examples suggest that the uncertainty levels may be reduced with the mechanism.

Probabilistic rough set approach defines the positive, negative and boundary regions, each associated with a certain level of uncertainty. A pair of threshold values determines the uncertainty levels in these regions. A critical issue in the community is the determination of optimal values of these thresholds. This problem may be investigated by considering a possible relationship between changes in probabilistic thresholds and their impacts on uncertainty levels of different regions. We investigate the use of game-theoretic rough set (GTRS) model in exploring such a relationship. A threshold configuration mechanism is defined with the GTRS model in order to minimize the overall uncertainty level of rough set based classification. By realizing probabilistic regions as players in a game, a mechanism is introduced that repeatedly tunes the parameters in order to calculate effective threshold parameter values. Experimental results on text categorization suggest that the overall uncertainty of probabilistic regions may be reduced with the threshold configuration mechanism.