Title :
Multi-granulation probabilistic rough set model
Author :
Yuejin, L.V. ; Qingmei Chen ; Lisha Wu
Author_Institution :
Coll. of Math. & Inf. Sci., Guangxi Univ., Nanning, China
Abstract :
This paper combines the probabilistic rough set with the multi-granulation rough set in the multi-granulation space. Then the models of optimistic multi-granulation probabilistic rough set and pessimistic multi-granulation probabilistic rough set are established, respectively. Some properties of these models are investigated. This paper analyzes the knownledge classification accuracy of multi-granulation rough set with probability distributions theory, the approximate accuracies are increased when comparing with Qian´s multi-granulation rough set. Finally, rationality and feasibility of the theory is verified by an example.
Keywords :
approximation theory; rough set theory; statistical distributions; approximate accuracies; knowledge classification; multigranulation probabilistic rough set model; multigranulation rough set; multigranulation space; optimistic multigranulation probabilistic rough set; pessimistic multigranulation probabilistic rough set; probability distributions theory; Accuracy; Adaptation models; Approximation methods; Information systems; Mathematical model; Probabilistic logic; Probability distribution; multi-granulation probabilistic rough set; multi-granulation rough sets; probabilistic rough set; rough set;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/FSKD.2013.6816183
