摘要
The article is a comprehensive review of two major approaches to rough set theory:the classic rough set model introduced by Pawlak and the probabilistic approaches.The classic model is presented as a staging ground to the discussion of two varieties of the probabilistic approach,i.e.of the variable precision and Bayesian rough set models.Both of these models extend the classic model to deal with stochastic interactions while preserving the basic ideas of the original rough set theory,such as set approximations,data dependencies,reducts etc.The probabilistic models are able to handle weaker data interactions than the classic model,thus extending the applicability of the rough set paradigm.The extended models are presented in considerable detail with some illustrative examples.
The article is a comprehensive review of two major approaches to rough set theory: the classic rough set model introduced by Pawlak and the probabilistic approaches. The classic model is presented as a staging ground to the discussion of two varieties of the probabilistic approach, i.e. of the variable precision and Bayesian rough set models. Both of these models extend the classic model to deal with stochastic interactions while preserving the basic ideas of the original rough set theory, such as set approximations, data dependencies, reducts etc. The probabilistic models are able to handle weaker data interactions than the classic model, thus extending the applicability of the rough set paradigm. The extended models are presented in considerable detail with some illustrative examples.
出处
《重庆邮电大学学报(自然科学版)》
2008年第3期254-265,共12页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
关键词
粗糙集
或然率
数学理论
计算方法
rough sets
variable precision rough sets
Bayesian rough sets
data dependencies
probabilistic rough sets