关于属性测度空间粗糙集模型的一个注记

文[1]关于属性粗糙集的定义中,上、下近似算子分别由等价类中元素属性测度的上、下确界来确定,没有充分反映等价类中其它元素的作用,在信息处理中不免造成元素信息的丢失。为此提出一种新的属性粗糙集近似算子的表示方法,给出了相应的性质,并实例说明了这一方法的合理性。     

覆盖粗糙集属性约简的新算法

覆盖广义粗糙集是Pawlak粗糙集的重要推广,其属性约简是粗糙集理论中最重要的问题之一.Tsang等基于一种生成覆盖设计了覆盖信息系统属性约简算法,但并未明确指出其适用的覆盖粗糙集类型.在本文中,我们首先指出Tsang的属性约简算法适用的覆盖粗糙集是第五,第六和第七类.其次,我们通过建立覆盖与自反且传递的二元关系之间的等价关系,提出了一种时间复杂度更低的属性约简算法,并证明了本文中的属性约简方法就是Wang等所提出的一般二元关系属性约简的特例.本文不仅提出了属性约简的简化算法,还首次建立起覆盖属性约简与二元关系属性约简之间的联系,具有理论和实际的双重意义.     

属性模糊集的分解特征

在模糊集理论研究的基础上,结合粗糙集的属性集、元素迁移的相关知识,提出属性模糊集的概念,利用给出属性模糊集的有关知识,结合模糊集与粗糙集合的有关理论,得到了属性模糊集的一系列重要定理,为模糊集合理论的进一步研究奠定了理论基础.     

属性测度空间的粗糙集模型

在属性测度空间理论指导下,提出了属性测度下的属性粗糙集模型及依参量的属性粗糙集模型,并就不同属性粗糙集模型的精度进行了讨论.     

Sp-下阶梯知识挖掘及状态识别

单向Sp-粗集中,具有属性集α的知识[x](R-元素等价类[x])具有这样的特征:若α内部分属性被删除,则[x]内的元素个数增加;利用这一特征,考虑属性被删除的随机性,给出Sp-下阶梯知识,Sp-下阶梯知识的依信度生成,Sp-下阶梯知识属性依赖的原理,给出Sp-下阶梯知识的属性依赖挖掘定理,Sp-下阶梯知识的状态识别算法.     

粗糙集理论在属性约简及知识分类中的应用

本针对不完备信息系统属性约简的两种定义，证明了两的等价性。在此基础上结合粗糙集理论提出了相似矩阵、相似区间的概念，并将其应用于不完备信息系统知识分类的问题中。     

基于精度的属性约简新方法

自Pawlak提出粗糙集概念以来,人们就一直对粗糙集的近似精度很感兴趣,出现了不少有关近似精度的文献.在粗糙集理论中,精度是量化由粗糙集边界引起的不精确性的一种重要数字特征.在分析传统精度和基于等价关系图的过剩熵的近似精度的基础上,提出了一种新的精度定义.比较发现,新定义的精度更具有合理性.同时把这个新定义的精度运用到了属性约简上,通过实例比较发现,本文提出的属性约简更具有可行性.     

基于信息量的集值信息系统的属性约简

属性约简是粗糙集理论研究的核心内容之一.在集值信息系统中引入信息量和属性重要性,给出它们的性质及与属性约简之间的关系.针对集值信息系统提出了一种基于信息量和属性重要性的属性约简算法及算法的时间复杂度.通过实例说明,该算法是有效的.     

基于粗集与概念格的属性简约讨论

概念格的属性简约是在形式背景下解决复杂问题的重要途径,通过对概念格、粗糙集的讨论,将两者有效结合,并借助粗糙集上(下)近似的方法,得出了一个对概念格属性简约的方法,方法将二维的概念格属性简约转化为一维的一种对象格的简约,避免了形式背景下的概念的计算和进一步的可辨识矩阵的计算,方法简便,算法简单易实现,是概念格属性简约有效的算法.     

基于模糊聚类的属性匹配算法

提出了一种基于模糊聚类的属性匹配算法。该算法采用能综合反映属性名称相似性和语义相似性的模糊相似关系,提高了属性匹配的准确率;以等价闭包法对相似属性进行模糊聚类,得到多层次属性分类结果,更客观真实地反映了属性匹配的模糊性;同时,属性匹配过程中不需要设置匹配参数,避免了人为造成的误差。     

Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets

Covering rough sets generalize traditional rough sets by considering coverings of the universe instead of partitions, and neighborhood-covering rough sets have been demonstrated to be a reasonable selection for attribute reduction with covering rough sets. In this paper, numerical algorithms of attribute reduction with neighborhood-covering rough sets are developed by using evidence theory. We firstly employ belief and plausibility functions to measure lower and upper approximations in neighborhood-covering rough sets, and then, the attribute reductions of covering information systems and decision systems are characterized by these respective functions. The concepts of the significance and the relative significance of coverings are also developed to design algorithms for finding reducts. Based on these discussions, connections between neighborhood-covering rough sets and evidence theory are set up to establish a basic framework of numerical characterizations of attribute reduction with these sets.     

Reduction about approximation spaces of covering generalized rough sets

The introduction of covering generalized rough sets has made a substantial contribution to the traditional theory of rough sets. The notion of attribute reduction can be regarded as one of the strongest and most significant results in rough sets. However, the efforts made on attribute reduction of covering generalized rough sets are far from sufficient. In this work, covering reduction is examined and discussed. We initially construct a new reduction theory by redefining the approximation spaces and the reducts of covering generalized rough sets. This theory is applicable to all types of covering generalized rough sets, and generalizes some existing reduction theories. Moreover, the currently insufficient reducts of covering generalized rough sets are improved by the new reduction. We then investigate in detail the procedures to get reducts of a covering. The reduction of a covering also provides a technique for data reduction in data mining.     

知识的属性扰动与属性扰动定理

By using the dynamic characteristic of one direction S-rough sets(one direction singular rough sets) and dual of one direction S-rough sets(dual of one direction singular rough sets),the concepts of attribute disturbance of knowledge,the attribute disturbance degree of knowledge,and the disturbance coefficient of knowledge are given.By employing these concepts,the cardinal order theorem of the attribute disturbance knowledge,the unit circle theorem of the attribute disturbance knowledge,and the discernib...     

模糊信息系统属性重要性度量

利用包含度工具将粗糙集方法应用在模糊信息系统中,给出了模糊信息系统中属性重要性度量计算方法,通过举例说明了[3]中关于属性重要性度量概念的局限性。     

不完备信息下的灰色粗糙集成决策模型侯亚林

In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.     

Dependence-space-based attribute reductions in inconsistent decision information systems

In rough set theory, attribute reduction is an important mechanism for knowledge discovery. This paper mainly deals with attribute reductions of an inconsistent decision information system based on a dependence space. Through the concept of inclusion degree, a generalized decision distribution function is first constructed. A decision distribution relation is then defined. On the basis of this decision distribution relation, a dependence space is proposed, and an equivalence congruence based on the indiscernibility attribute sets is also obtained. Applying the congruences on a dependence space, new approaches to find a distribution consistent set are formulated. The judgement theorems for judging distribution consistent sets are also established by using these congruences and the decision distribution relation.     

一种基于Rough理论的知识推理冲突消解策略

知识推理中的冲突消解,不仅受到知识粒度的影响,还与所采用的具体方法有关.首先讨论了应用Rough理论对规则的粒度进行客观刻画,在此基础上采用距离概念构造了刻画规则相近性的规则距离公式,并在公式中引入了Rough理论中属性重要性算子,然后在分析传统冲突消解策略的基础上,提出了知识推理过程中规则集进化的概念,最后通过实例说明了此策略在知识推理中的具体应用.