粗集理论的矩阵方法

粗糙集理论是近年来发展起来的一种有效的处理不精确、不确定信息的理论,在机器学习及数据挖掘等领域获得了成功的应用,该文用矩阵的方法来研究粗糙集,即从一个二元关系矩阵出发,给出粗糙集上下近似的矩阵描述,实际上是用矩阵的方法重新定义上下近似,矩阵的方法不仅提供了上下近似的简单的计算方法,也提供了一种新的推理的方法,我们还把矩阵方法用于信息系统的约简。     

一种基于粗糙集的信息系统决策规则提取方法

以粗糙集理论为基础,引入相似性的概念,并提出其衡量方法,改进了粗糙集理论中不可辨关系的确定条件,给出了基于新的相似关系的上下近似空间定义,并举例说明了基于粗糙集的相似性规则提取方法。     

两个论域上的粗集结构

粗集理论是处理不精确和不确定的数据的工具,自Pawlak 提出了粗集理论后,粗集模型得到拓广,人们提出了许多新的粗集模型,在用特征函数的方法表示上下近似的基础上研究两个论域上的粗集结构。统一了粗集的各种推广模型,使得特征函数的方法与通常的集合论的方法形成互补,对粗集结构的简化及推理有帮助,可以加深对粗集结构的认识。     

On intuitionistic fuzzy rough sets and their topological structures

In this paper, lower and upper approximations of intuitionistic fuzzy sets with respect to an intuitionistic fuzzy approximation space are first defined. Properties of intuitionistic fuzzy approximation operators are examined. Relationships between intuitionistic fuzzy rough set approximations and intuitionistic fuzzy topologies are then discussed. It is proved that the set of all lower approximation sets based on an intuitionistic fuzzy reflexive and transitive approximation space forms an intuitionistic fuzzy topology; and conversely, for an intuitionistic fuzzy rough topological space, there exists an intuitionistic fuzzy reflexive and transitive approximation space such that the topology in the intuitionistic fuzzy rough topological space is just the set of all lower approximation sets in the intuitionistic fuzzy reflexive and transitive approximation space. That is to say, there exists an one-to-one correspondence between the set of all intuitionistic fuzzy reflexive and transitive approximation spaces and the set of all intuitionistic fuzzy rough topological spaces. Finally, intuitionistic fuzzy pseudo-closure operators in the framework of intuitionistic fuzzy rough approximations are investigated.     

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.     

Generalized rough sets based on relations

Rough set theory has been proposed by Pawlak as a tool for dealing with the vagueness and granularity in information systems. The core concepts of classical rough sets are lower and upper approximations based on equivalence relations. This paper studies arbitrary binary relation based generalized rough sets. In this setting, a binary relation can generate a lower approximation operation and an upper approximation operation, but some of common properties of classical lower and upper approximation operations are no longer satisfied. We investigate conditions for a relation under which these properties hold for the relation based lower and upper approximation operations.This paper also explores the relationships between the lower or the upper approximation operation generated by the intersection of two binary relations and those generated by these two binary relations, respectively. Through these relationships, we prove that two different binary relations will certainly generate two different lower approximation operations and two different upper approximation operations.     

CD格上的近似算子

Pawlak近似算子具有多种推广形式。讨论了完全分配格上的近似算子。通过近似空间中的不确定性映射,分别引入了三种形式的上近似算子及下近似算子,讨论了它们的基本性质及其与已有近似算子之间的关系。研究结果表明,目前文献中出现的多种近似算子可以作为完全分配格上近似算子的特例。     

模糊近似空间上的粗糙模糊集的公理系统

粗糙集理论是近年来发展起来的一种有效的处理不精确、不确定、含糊信息的理论，在机器学习及数据挖掘等领域获得了成功的应用．粗糙集的公理系统是粗糙集理论与应用的基础．粗糙模糊集是粗糙集理论的自然的有意义的推广．作者研究了模糊近似空间上的粗糙模糊集的公理系统，用三条简洁的相互独立的公理完全刻划了模糊近似空间上的粗糙模糊集，同时还把作者给出的公理系统与粗糙集的公理系统做了对比，指出了两者的区别．     

Soft fuzzy rough sets and its application in decision making

Recently, the theory and applications of soft set has brought the attention by many scholars in various areas. Especially, the researches of the theory for combining the soft set with the other mathematical theory have been developed by many authors. In this paper, we propose a new concept of soft fuzzy rough set by combining the fuzzy soft set with the traditional fuzzy rough set. The soft fuzzy rough lower and upper approximation operators of any fuzzy subset in the parameter set were defined by the concept of the pseudo fuzzy binary relation (or pseudo fuzzy soft set) established in this paper. Meanwhile, several deformations of the soft fuzzy rough lower and upper approximations are also presented. Furthermore, we also discuss some basic properties of the approximation operators in detail. Subsequently, we give an approach to decision making problem based on soft fuzzy rough set model by analyzing the limitations and advantages in the existing literatures. The decision steps and the algorithm of the decision method were also given. The proposed approach can obtain a object decision result with the data information owned by the decision problem only. Finally, the validity of the decision methods is tested by an applied example.     

积模糊粗集模型及其模糊知识粒的表示和分解

为处理人工智能中不精确和不确定的数据和知识,Pawlak提出了粗集理论。之后粗集理论被推广,其方法主要有二:一是减弱对等价关系的依赖;二是把研究问题的论域从一个拓展到多个。结合这两种思想,研究基于两个模糊近似空间的积模糊粗集模型及其模糊粗糙集的表示和分解。根据这种思想,可以从论域分解的角度探索降低高维模糊粗糙集计算的复杂度问题。先对模糊近似空间的分层递阶结构———λ-截近似空间进行研究,得到不同层次知识粒的相互关系;然后定义模糊等价关系的积,并研究其性质及算法;最后构建基于积模糊等价关系的积模糊粗集模型,并讨论了该模型中模糊粗糙集的表示及分解问题,分别从λ-截近似空间和一维模糊近似空间的角度去处理,给出了可分解集的上(下)近似的一个刻画,及模糊可分解集的上(下)近似的λ-截集分解算法。     

Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision‐making

Probabilistic approaches to rough sets are still an important issue in rough set theory. Although many studies have been written on this topic, they focus on approximating a crisp concept in the universe of discourse, with less effort on approximating a fuzzy concept in the universe of discourse. This article investigates the rough approximation of a fuzzy concept on a probabilistic approximation space over two universes. We first present the definition of a lower and upper approximation of a fuzzy set with respect to a probabilistic approximation space over two universes by defining the conditional probability of a fuzzy event. That is, we define the rough fuzzy set on a probabilistic approximation space over two universes. We then define the fuzzy probabilistic approximation over two universes by introducing a probability measure to the approximation space over two universes. Then, we establish the fuzzy rough set model on the probabilistic approximation space over two universes. Meanwhile, we study some properties of both rough fuzzy sets and fuzzy rough sets on the probabilistic approximation space over two universes. Also, we compare the proposed model with the existing models to show the superiority of the model given in this paper. Furthermore, we apply the fuzzy rough set on the probabilistic approximation over two universes to emergency decision‐making in unconventional emergency management. We establish an approach to online emergency decision‐making by using the fuzzy rough set model on the probabilistic approximation over two universes. Finally, we apply our approach to a numerical example of emergency decision‐making in order to illustrate the validity of the proposed method.     

覆盖模糊S-粗集模型

建立了基于覆盖理论的模糊S-粗糙集模型,并讨论其性质。在覆盖单向S-粗集x的最小描述的基础上,给出了x的最大描述的定义。给出了覆盖模糊S-粗集上 、下近似算子定义,讨论了算子的基本性质,证明了覆盖S-粗糙集模型下所有模糊集的下近似构成一个模糊拓扑,并得到模糊单向S-粗集X相对于覆盖单向S-粗集和覆盖约简单向S-粗集的上下近似分别相等。     

基于概念格的粗糙集近似

粗糙集理论的一个重要研究方面是用已定义的概念来近似未定义的概念,而如何构建可定义概念以及如何确定近似运算是这一工作的基础.利用粗糙集这一工具,从概念格的角度来确定可定义概念,并在此基础上研究了概念的粗糙近似.根据粗糙集上下近似的包含关系,得到概念的一种新的上下近似的运算的定义.粗糙集近似理论利用两种不同的近似运算,产生两种不同的近似来描述概念格背景下的对象集合.     

变精度上近似与程度下近似粗糙集模型的正域及其算法

针对变精度近似与程度近似的结合问题及正域的核心地位,组建了变精度上近似与程度下近似粗糙集模型,并定义了其中的正域概念。研究了模型正域与精度量化指标和程度量化指标关联的内涵及意义,得到了模型正域的精确刻画与性质。为了计算模型正域,提出了自然算法与原子算法,并进行了算法分析与算法比较,得到了自然算法与原子算法具有相同的时间复杂性,而原子算法却具有更优的空间复杂性的结论。最后用一个医疗实例对模型正域及其算法进行了分析与说明。变精度上近似与程度下近似粗糙集模型的正域,从膨胀的优势方向完全扩展了经典粗糙集模型的正域,对与精度参数和程度参数相关的必然性知识发现具有意义。     

Dynamic Maintenance of Approximations in Dominance‐Based Rough Set Approach under the Variation of the Object Set

Dominance‐based rough sets approach (DRSA) is an effective tool to deal with information with preference‐ordered attribute domains and decision classes. Any information system may evolve when new objects enter into or old objects get out. Approximations of DRSA need update for decision analysis or other relative tasks. Incremental updating is a feasible and effective technique to update approximations. The purpose of this paper is to present an incremental approach for updating approximations of DRSA. The approach is applicable to dynamic information systems when the set of objects varies over time. In this paper, we discuss the principles of incrementally updating P‐dominating sets and P‐dominated sets and propose an incremental approach for updating approximations of DRSA. A numerical example is given to illustrate the incremental approach. The experimental evaluations on data sets from UCI show that the incremental approach outperforms the original nonincremental one.     

含序信息的粗集方法研究

经典粗集理论给出了不可识别、上近似、下近似、简式和核等概念,其核心思想是运用条件属性集导致的知识粒子来近似决策属性集导致的知识粒子,进而推导出规则。这些知识粒子的实质是根据存在于属性值问的等价关系得到的,而事实上可能存在某些属性,其属性值内部存在序关系,与其它某属性间存在语义关系,这样的属性称为标准。本文所研究的粗集方法,考虑标准所携带的这些信息,推导出含有序信息的规则,并探讨使推导的规则更加完全和一致。本文给出了含序粗集方法（CORS）的定义、数据分析以及规则生成方法,并提出了一种更加合理的质量近似公式以及生成规则的四条原则。     

Interval set clustering

Rough k-means clustering describes uncertainty by assigning some objects to more than one cluster. Rough cluster quality index based on decision theory is applicable to the evaluation of rough clustering. In this paper we analyze rough k-means clustering with respect to the selection of the threshold, the value of risk for assigning an object and uncertainty of objects. According to the analysis, clusters presented as interval sets with lower and upper approximations in rough k-means clustering are not adequate to describe clusters. This paper proposes an interval set clustering based on decision theory. Lower and upper approximations in the proposed algorithm are hierarchical and constructed as outer-level approximations and inner-level ones. Uncertainty of objects in out-level upper approximation is described by the assignment of objects among different clusters. Accordingly, ambiguity of objects in inner-level upper approximation is represented by local uniform factors of objects. In addition, interval set clustering can be improved to obtain a satisfactory clustering result with the optimal number of clusters, as well as optimal values of parameters, by taking advantage of the usefulness of rough cluster quality index in the evaluation of clustering. The experimental results on synthetic and standard data demonstrate how to construct clusters with satisfactory lower and upper approximations in the proposed algorithm. The experiments with a promotional campaign for the retail data illustrates the usefulness of interval set clustering for improving rough k-means clustering results.     

粗糙模糊集的构造与公理化方法

用构造性方法和公理化研究了粗糙模糊集．由一个一般的二元经典关系出发构造性地定义了一对对偶的粗糙模糊近似算子，讨论了粗糙模糊近似算子的性质，并且由各种类型的二元关系通过构造得到了各种类型的粗糙模糊集代数．在公理化方法中，用公理形式定义了粗糙模糊近似算子，各种类型的粗糙模糊集代数可以被各种不同的公理集所刻画．阐明了近似算子的公理集可以保证找到相应的二元经典关系，使得由关系通过构造性方法定义的粗糙模糊近似算子恰好就是用公理化定义的近似算子。     

A roughness measure of fuzzy sets from the perspective of distance

Covering generalized rough set theory is an important extension of classical rough set theory. To characterize a fuzzy set in a given covering approximation space, a pair of fuzzy sets, called covering rough fuzzy lower and upper approximations, were introduced, but they do not describe well how much uncertainty is induced by the granularity of knowledge. In this paper, we first discuss the relationship between uncertainty and granularity of knowledge. Then we examine several commonly used distance measures, and indicate that some of them exhibit some limitations. Next we propose a roughness measure based on Minkowski distance, and examine some important properties of this measure. Finally, an illustrative example is provided to demonstrate the application of the roughness measure to incomplete information systems with fuzzy decision.     

Generalized rough sets over fuzzy lattices

This paper studies generalized rough sets over fuzzy lattices through both the constructive and axiomatic approaches. From the viewpoint of the constructive approach, the basic properties of generalized rough sets over fuzzy lattices are obtained. The matrix representation of the lower and upper approximations is given. According to this matrix view, a simple algorithm is obtained for computing the lower and upper approximations. As for the axiomatic approach, a set of axioms is constructed to characterize the upper approximation of generalized rough sets over fuzzy lattices.