Rough sets: the classical and extended views
The article is a comprehensive review of two major approaches to rough set theory:the classic rough setmodel introduced by Pawlak and the probabilistic approaches. The classic model is presented as a staging ground tothe discussion of two varieties of the probabilistic approach, i. e.of the variable precision and Bayesian rough setmodels. Both of these models extend the classic model to deal with stochastic interactions while preserving the basicideas 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 therough set paradigm. The extended models are presented in considerable detail with some illustrative examples.
rough sets variable precision rough sets Bayesian rough sets data dependencies probabilistic rough
ZIARKO Wojciech
Department of Computer Science,University of Regina,SK A4S 0A2, Canada
国内会议
重庆
英文
254-265
2008-05-13（万方平台首次上网日期，不代表论文的发表时间）