The rough sets can be used to induce three-way classification decisions. The positive negative and boundary regions can be interpreted as regions of acceptance, rejection and deferment decisions, respectively. The probabilistic rough set model extends the conventional rough sets by providing more effective way for classifying objects. A main result of probabilistic rough sets is the interpretation of three-way decisions using a pair of probabilistic thresholds. The game-theoretic rough set model determine and interprets the required thresholds by utilizing a game-theoretic environment for analyzing strategic situations between cooperative or conflicting decision making criteria. The essential idea is to implement a game for investigating how the probabilistic thresholds may change in order to improve the rough set based decision making.
^N. Azam, J. T. Yao, Analyzing Uncertainties of Probabilistic Rough Set Regions with Game-theoretic Rough Sets, International Journal of Approximate Reasoning, Vol. 55, No.1, pp 142-155, 2014.
^Y. Zhang, Optimizing Gini Coefficient of Probabilistic Rough Set Regions using Game-Theoretic Rough Sets, Proceeding of 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE'13), Regina, Canada, May 5–8, 2013, pp 699–702
^J.T. Yao, J.P. Herbert, A Game-Theoretic Perspective on Rough Set Analysis, 2008 International Forum on Knowledge Technology (IFKT'08), Chongqing, Journal of Chongqing University of Posts and Telecommunications, Vol. 20, No. 3, pp 291–298, 2008.
^Y. Zhang, J.T. Yao, Rule Measures Tradeoff Using Game-theoretic Rough Sets, Proceeding of the International Conference on Brian Informatics (BI'12), Macau, China, Dec 4–7, 2012, Lecture Notes in Computer Science 7670, pp 348–359.