Rougness, rough entropy, fuzziness, and fuzzy entropy are major methods for measuring the uncertainty of rough sets.
粗糙集的不确定性度量方法，目前主要包括粗糙集的粗糙度、粗糙熵、模糊度和模糊熵。
The image threshold segmentation algorithm based on the Particle Swarm Optimization (PSO) combined with the rough entropy based on boundary region is presented.
提出一种基于微粒群优化(PSO)的边界区域粗糙熵的阈值图像分割算法。
The concept of rough entropy is proposed. The monotony between the uncertainty of knowledge in the rough set theory and its corresponding rough entropy is proved.
提出了粗糙信息熵的概念，证明了粗糙集理论中知识不确定性与其所对应的粗糙信息熵之间的单调关系。
Moreover, the modified rough entropy and the fuzzy entropy based on equivalence relation is extended to the generalized modified rough entropy and the generalized fuzzy entropy.
并分别将基于不可分辨关系下的修正粗糙熵和模糊熵拓展到基于一般二元关系下的广义修正粗糙熵和广义模糊熵。
The analysis and real example show that the modified rough entropy method for measuring uncertainty in rough sets is more reasonable and more accurate than the classical methods.
通过分析和实例可以看出，所提出的修正粗糙熵方法可以用来更合理、更精确地测量粗糙集中的不确定性。
The rough entropy of the uncertainty of ordinary set and fuzzy set, and the monotonous relation between the uncertainty of these two kinds of set and their corresponding rough entropy, are discussed.
并研究了与普通集合和模糊集合的不确定性相对应的粗糙信息熵，以及这两种集合的不确定性与其对应的粗糙信息熵之间的单调关系。
The main contents of the dissertation are as follows: the entropy methods to measure the uncertainty and the fuzziness in rough sets are proposed.
全文的主要内容如下：针对粗糙集中存在的不确定性和模糊性，提出了新的熵测量方法。
Using fuzzy aggregation theory and rough set theory, this article puts out a weight allocation method based on impersonal message entropy.
借助模糊聚类技术和粗糙集理论提出了一个基于客观信息熵的多因素权重分配方法。
Four kinds of condition entropy are defined in this paper. Accordingly, four kinds of entropy based methods for the attribute reduction in the rough set data analysis are proposed.
本文定义了四种条件熵，并在此基础上提出了四种基于熵的方法，以用于粗糙集数据分析中的属性简约。
A method with the fuzzy entropy for measuring fuzziness to fuzzy problem in rough sets is proposed.
针对粗糙集中存在的模糊性问题，提出了一种利用模糊熵测量其模糊性的方法。
From the rough sets theory, this text discusses the relations between the granularity, resolution and entropy.
论文从传统粗集理论出发，讨论在粗集理论中粒度、分辨度与熵的关系。
Then the membership matrix obtained by clustering algorithm was used to reduce attribute set. Finally, based on entropy, a knowledge acquisition method of fuzzy Rough Set (RS) was put forward.
进而将聚类得到的属性隶属矩阵用于属性约简，并提出一种基于信息熵的模糊粗糙集知识获取的方法。
We propose a new approach to multivariate decision tree construction based on knowledge roughness in rough set instead of information entropy as usual.
提出了一种基于粗糙集中知识粗糙度的构建多变量决策树的算法。
We propose a new approach to multivariate decision tree construction based on knowledge roughness in rough set instead of information entropy as usual.
提出了一种基于粗糙集中知识粗糙度的构建多变量决策树的算法。
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