【摘要】 从不协调信息系统中获取知识是一个研究热点,变精度粗糙集理论模型把集合的关系拓展到"一定程度包含",从而将经典粗糙集理论模型中划归于边界域中的元素拓展到正域结构中,在一定程度上提高了获取异常信息的能力.在经典粗糙集理论模型的基础上,讨论了协调集的构造及其修正定义,并进一步讨论了变精度粗糙集模型理论中协调集的定义;提出了基于协调集的决策树构造方法.结果表明沿着树的分支序贯能得到规则的合理解释,但比传统意义上的决策树要简单.更多还原

【Abstract】 As an important mathematic method to deal with knowledge fuzziness and knowledge uncertainty,rough sets theory has been paid more and more attention and widely used in various fields such as data mining,attributes reduction with decision tree one of the most essential application.Though decision tree algorithm of rough sets theory have obtained many essential achievements,there are still some important issues need to be further resolved,especially the problems reflected in the low-efficiency about knowledge.Nowadays,it becomes a common interest for us to find how to enrich the knowledge about information system.However,Classical rough set theory assumed that the information systems are complete,but in real applications,many information systems are incomplete or inconsistent because of different reasons.Therefore,knowledge acquisition from inconsistent information systems is a hot research.The more relaxed formulation of lower approximation in terms of controlled degree of overlap between sets rather than the inclusion relation was introduced in the context of the variable precision rough sets model.So the lower and upper approximations could be naturally interpreted in probabilistic terms,leading to generalized notions of rough set approximations,which will expand the elements of the boundary regions to the positive regions of Pawlak rough sets theory and improve the ability of obtaining the exception information.In this paper,the definitions about consistent set and the revised definition are discussed based on Pawlak rough sets theory,and further redefined the variable precision consistent set.An approach for constructing the decision tree based on consistent set is proposed.The results show that the sequential branches along the decision tree will get reasonable rules,which is simpler than the traditional decision tree.更多还原