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.     

一种新的诱导覆盖粗糙集

定义了一种新的诱导覆盖粗糙集,这种定义可以保证其满足对偶性.然后证明了该诱导覆盖粗糙集具备的性质.最后讨论了两种诱导覆盖粗糙集之间的关系.     

覆盖近似空间的连续与同胚映射

利用一般映射研究了覆盖近似空间的一些性质,并证明了一些结论.接着定义了覆盖空间的粗糙连续映射及粗糙同胚映射.最后在覆盖粗糙连续映射和覆盖粗糙同胚映射的条件下,研究了两个覆盖近似空间的有关性质,进而在某种程度上为覆盖近似空间的分类提供了理论依据.     

C/E系统的交替行为特性

本文将C/E系统中事件交替实施定义为二元关系ALT,并定义一种系统基本现象:广义冲突.随后,导出了系统的交替行为特性以及二元关系ALT可传递的充分必要条件.     

广义近似空间的拓扑性质

本文研究了广义近似空间的拓扑性质。首先证明了论域上任意二元关系均能诱导一个Alexandrov拓扑,并讨论了该拓扑的若干性质。其次给出了论域上两个不同二元关系诱导同一个拓扑的充分必要条件。最后在全体二元关系之集上定义了一个等价关系,并研究了等价类的结构。     

基于最小描述交的覆盖广义粗糙集

针对Bonikowski覆盖广义粗糙集模型的不足,给出了基于最小描述史的覆盖上下近似算子.通过和Pawlak经典粗糙集以及Bonikowski的覆盖广义粗糙集比较,发现给出的覆盖上、下近似算子具有了对偶关系,并得到了相关重要性质;进一步讨论了在新定义下覆盖广义粗糙集的约简和公理化问题,丰富了覆盖广义粗糙条理论,并为覆盖广义粗糙集的应用提供了更确切的理论根据.     

覆盖粗糙集的不确定性度量研究进展

覆盖粗糙集作为经典粗糙集一种较为流行的扩充模型,其现有不确定性度量方法主要包括覆盖粒度、粗糙度、粗糙熵、模糊度和模糊熵等。本文从纯粗糙集、信息论和模糊性三个视角对覆盖粗糙集的不确定性度量方法进行了分类梳理,通过结合覆盖粒度对覆盖粗糙度、覆盖精确度和覆盖粗糙熵进行了修正定义;设计了基于最小描述交的隶属函数,结合隶属函数对覆盖模糊度和覆盖模糊熵重新定义,给出了相关推论,分析了相关性质,为后续研究覆盖粗糙集不确定性的相关问题提供了新思路。     

完全分配格上的覆盖粗糙集模型

将集合论中的覆盖概念抽象到完全分配格L上,利用它定义格L上关于覆盖的上(下)近似算子,给出格L上覆盖粗糙集模型.文中先讨论格L上覆盖的相关性质,进而研究了覆盖上(下)近似算子的性质,得到若干结果.     

幺半环上模糊有限状态自动机积的覆盖关系

提出了幺半环上模糊有限状态自动机的各种乘积以及覆盖的定义,并得到了一些性质.证明了直积、级联积、圈积三种乘积以及和之间的覆盖关系,得到了乘积自动机、和自动机覆盖关系的一些代数性质.     

基于子基的覆盖拓扑空间的序列紧致性

基于子基的覆盖拓扑空间主要将覆盖所成子基引入粗糙集框架来诱导变异拓扑,已经具有连通性、分离性、可数性与紧致性的性质研究,但尚未涉及序列紧致性讨论.本文主要探讨基于子基的覆盖拓扑空间的序列紧致性.针对基于子基的覆盖拓扑空间,在深化紧致性的基础上,定义了序列紧致性,并研究了相关性质,最后提供一个说明实例.所得结果对基于子基的覆盖拓扑空间进行了系统完善与深入刻画.     

Covering rough sets based on neighborhoods: An approach without using neighborhoods

Rough set theory, a mathematical tool to deal with inexact or uncertain knowledge in information systems, has originally described the indiscernibility of elements by equivalence relations. Covering rough sets are a natural extension of classical rough sets by relaxing the partitions arising from equivalence relations to coverings. Recently, some topological concepts such as neighborhood have been applied to covering rough sets. In this paper, we further investigate the covering rough sets based on neighborhoods by approximation operations. We show that the upper approximation based on neighborhoods can be defined equivalently without using neighborhoods. To analyze the coverings themselves, we introduce unary and composition operations on coverings. A notion of homomorphism is provided to relate two covering approximation spaces. We also examine the properties of approximations preserved by the operations and homomorphisms, respectively.     

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.     

Four matroidal structures of covering and their relationships with rough sets

Covering is a common form of data representation, and covering-based rough sets serve as an efficient technique to process this type of data. However, many important problems such as covering reduction in covering-based rough sets are NP-hard so that most algorithms to solve them are greedy. Matroids provide well-established platforms for greedy algorithm foundation and implementation. Therefore, it is necessary to integrate covering-based rough set with matroid. In this paper, we propose four matroidal structures of coverings and establish their relationships with rough sets. First, four different viewpoints are presented to construct these four matroidal structures of coverings, including 1-rank matroids, bigraphs, upper approximation numbers and transversals. The respective advantages of these four matroidal structures to rough sets are explored. Second, the connections among these four matroidal structures are studied. It is interesting to find that they coincide with each other. Third, a converse view is provided to induce a covering by a matroid. We study the relationship between this induction and the one from a covering to a matroid. Finally, some important concepts of covering-based rough sets, such as approximation operators, are equivalently formulated by these matroidal structures. These interesting results demonstrate the potential to combine covering-based rough sets with matroids.     

Topology vs generalized rough sets

This paper investigates the relationship between topology and generalized rough sets induced by binary relations. Some known results regarding the relation based rough sets are reviewed, and some new results are given. Particularly, the relationship between different topologies corresponding to the same rough set model is examined. These generalized rough sets are induced by inverse serial relations, reflexive relations and pre-order relations, respectively. We point that inverse serial relations are weakest relations which can induce topological spaces, and that different relation based generalized rough set models will induce different topological spaces. We proved that two known topologies corresponding to reflexive relation based rough set model given recently are different, and gave a condition under which the both are the same topology.     

覆盖广义粗糙集的模糊性

在研究覆盖广义粗糙集的基础上,利用两个距离函数Hamming和Euclidean距离函数,结合模糊集的最近寻常集,引入了覆盖广义粗糙集模糊度的概念,给出了一种模糊度计算方法,并证明了该模糊度的一些重要性质。这些结果在覆盖广义粗糙集的理论研究和应用都发挥着一定作用。     

On some types of covering rough sets from topological points of view

The concept of coverings is one of the fundamental concepts in topological spaces and plays a big part in the study of topological problems. This motivates the research of covering rough sets from topological points of view. From topological points of view, we can get a good insight into the essence of covering rough sets and make our discussions concise and profound. In this paper, we first construct a type of topology called the topology induced by the covering on a covering approximation space. This notion is indeed in the core of this paper. Then we use it to define the concepts of neighborhoods, closures, connected spaces, and components. Drawing on these concepts, we define several pairs of approximation operators. We not only investigate the relationships among them, but also give clear explanations of the concepts discussed in this paper. For a given covering approximation space, we can use the topology induced by the covering to investigate the topological properties of the space such as separation, connectedness, etc. Finally, a diagram is presented to show that the collection of all the lower and upper approximations considered in this paper constructs a lattice in terms of the inclusion relation ⊆.     

广义覆盖粗集的约简

在保持一对覆盖上、下近似算子不变的条件下,探讨覆盖族的约简.利用所构造的辩识矩阵给出覆盖族的约简与核心的判别定理,并提出基于信息量的寻找最小约简的算法,从而进一步完善广义覆盖粗集的约简理论.     

On some types of neighborhood-related covering rough sets

Covering rough sets are natural extensions of the classical rough sets by relaxing the partitions to coverings. Recently, the concept of neighborhood has been applied to define different types of covering rough sets. In this paper, by introducing a new notion of complementary neighborhood, we consider some types of neighborhood-related covering rough sets, two of which are firstly defined. We first show some basic properties of the complementary neighborhood. We then explore the relationships between the considered covering rough sets and investigate the properties of them. It is interesting that the set of all the lower and upper approximations belonging to the considered types of covering rough sets, equipped with the binary relation of inclusion ?, constructs a lattice. Finally, we also discuss the topological importance of the complementary neighborhood and investigate the topological properties of the lower and upper approximation operators.     

基于泛系粗糙集等价算子上的粒计算模型

标准粗糙集使用等价类作为粒来描述概念.本文弱化对等价关系的要求, 将更广泛的粒计算模型建立到泛系粗糙集上去.本文通过对全域的分割和覆盖来诱导出泛系粗糙集上的粒计算模型.