Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

A Multi-Tuple Rough Set Approach for Information Retrieval

How to deal with the imprecise information retrieval has become more and more important in the present information society. An efficient and effective method of information retrieval based on multi tuple rough set is discussed in this paper. The new approach is considered as a generalization of the original rough set model for flexible information retrieval. The imprecise query results can be obtained by multi tuple approximations.

Approach to stochastic multi-attribute decision problems using rough sets theory

Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.

Cost Sensitive Multi-Granulation Preference Relation Rough Set for Ordinal Decision System

The preference analysis is a class of important issues in multi-criteria ordinal decision making.The rough set is an effective approach to handle preference analysis.In order to solve the multi-criteria preference analysis problems,this paper improves the preference relation rough set model and expands it to multi-granulation cases.Cost is also an important issue in the field of decision analysis.Taking the cost into consideration,we also expand the model to the cost sensitive multi-granulation preference relation rough set.Some theorems are represented,and the granule structure selection based on approximation quality is investigated.The experimental results show that the multi-granulation preference rough set approach with the consideration of cost has a better performance in granule structure selection than that without cost consideration.

2-Tuple and Rough Set Based Reduction Model for Multi-sensory Evaluation Indicators

In order to lessen adverse influences of excessive evaluative indicators of the initial set in multi-sensory evaluation,a2-tuple and rough set based reduction model is built to simplify the initial set of evaluative indicators. In the model,a great variety of descriptive forms of the multi-sensory evaluation are also taken into consideration. As a result,the method proves effective in reducing redundant indexes and minimizing index overlaps without compromising the integrity of the evaluation system. By applying the model in a multi-sensory evaluation involving community public information service facilities,the research shows that the results are satisfactory when using genetic algorithm optimized BP neural network as a calculation tool. It shows that using the reduced and simplified set of indicators has a better predication performance than the initial set,and 2-tuple and rough set based model offers an efficient way to reduce indicator redundancy and improves prediction capability of the evaluation model.

Rough set based multi-agent system cooperation for industrial supervisory interface system

In this paper, rough set theory is introduced into the interface multi-agent system （MAS） for industrial supervisory system. Taking advantages of rough set in data mining, a cooperation model for MAS is built. Rules for avoiding cooperation conflict are deduced. An optimization algorithm is used to enhance security and real time attributes of the system. An application based on the proposed algorithm and rules are given.

Research on Destriping Based on Rough Set

In order to remove the stripe noises in cotton foreign fiber images by line scanning camera collected, in multi threshold segmentation of rough set, every region’s color is instead of the statistics color of the region. This method can retain the detail information of original image as far as possible, and do well in the stripe noise removal. The roughness of rough set was calculated respectively using directional diagram, Canny operator and Sobel operator. Comparing the three methods, the results indicate that the Canny operator keeps the more details of image, and directional diagram and Sobel operator have the better effects on denoising.

Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM’training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.

基于平均型聚合函数的多粒度模糊粗糙集

多粒度模糊粗糙集(multi-granulation fuzzy rough set,MGFRS)是一种重要的粗糙集模型。乐观多粒度模糊粗糙集和悲观多粒度模糊粗糙集分别给出了被描述对象的两种极端描述。从数据聚合角度看,将不同粒度值合并为单一粒度值时,乐观MGFRS采用的是取小算子,悲观MGFRS采用的是取大算子。将取小与取大算子拓展为一般的平均型聚合算子,提出了一种新的多粒度模糊粗糙集模型:基于平均型聚合函数的多粒度模糊粗糙集(multi-granulation fuzzy rough set model on the base of average aggregate function,MFA)模型。研究了MFA模型的基本性质,给出了MFA模型与乐观和悲观多粒度模糊粗糙集的关系。同时,基于MFA模型,提出了基于平均型聚合算子的粗糙近似集模型。

Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects

In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.

广义多粒度双量化邻域粗糙集

针对实数型数据的信息量化问题,引入相对概念和绝对基数构建了广义多粒度双量化邻域粗糙集模型.首先,通过I型和II型广义多粒度上、下邻域特征支撑函数构建两类广义多粒度上、下邻域近似算子并讨论其性质;其次,讨论了两种广义多粒度邻域粗糙集的关系;最后,通过传染病案例实证分析了模型的实用性和有效性.

基于Spark和NRSCA策略的并行深度森林算法

针对并行深度森林在大数据环境下存在冗余及无关特征过多、两端特征利用率过低、模型收敛速度慢以及级联森林并行效率低等问题,提出了基于Spark和NRSCA策略的并行深度森林算法——PDF-SNRSCA。首先,该算法提出了基于邻域粗糙集和Fisher score的特征选择策略(FS-NRS),通过衡量特征的相关性和冗余度,对特征进行过滤,有效减少了冗余及无关特征的数量;其次,提出了一种随机选择和等距提取的扫描策略(S-RSEE),保证了所有特征能够同概率被利用,解决了多粒度扫描两端特征利用率低的问题;最后,结合Spark框架,实现级联森林并行化训练,提出了基于重要性指数的特征筛选机制(FFM-II),筛选出非关键性特征,平衡增强类向量与原始类向量维度,从而加快模型收敛速度,同时设计了基于SCA的任务调度机制(TSM-SCA),将任务重新分配,保证集群负载均衡,解决了级联森林并行效率低的问题。实验表明,PDF-SNRSCA算法能有效提高深度森林的分类效果,且对深度森林并行化训练的效率也有大幅提升。

基于多元组Rough集的不相容决策

Ｒｏｕｇｈ集理论是一种具有模糊边界的集合理论．它被广泛应用于不相容决策表中的规则提取．本文讨论了基于多元组Ｒｏｕｇｈ集的不相容决策方法，并给出了相应的算法，同时也对不相容规则的相容度作了分析．该方法比普通Ｒｏｕｇｈ集方法更简单、更有效．

一种基于遗传算法的Rough集多知识抽取方法

Rough集理论为知识约简提供了一种有效的方法.提出了一种基于遗传算法的Rough集多知识抽取方法.针对决策系统中知识约简的不唯一性,构造了一种多约简算法,创建了多知识.在此基础上,利用遗传算法从一个更高的层次对多知识进行优化,并从中抽取最优知识集.试验结果分析表明,通过遗传算法优化后抽取的多知识较单体知识具有更高的精度,使知识的表示更具广义性.

基于区间值犹豫模糊多粒度粗糙集的三支决策模型

针对区间值犹豫模糊信息系统,提出了两种基于区间犹豫模糊多粒度粗糙集的三支决策模型。首先,借助多粒度粗糙集理论,提出乐观和悲观区间犹豫模糊多粒度粗糙集模型。其次,引入区间犹豫模糊连续交叉熵的概念,用TOPSIS方法来计算对象在不同情况下的条件概率。基于此,定义区间犹豫模糊决策理论粗糙集并给出相应的三支决策规则。最后,通过实例验证了这两种模型对目标评估采取不同的态度和决策方案,并且证明了所提算法的有效性。

多尺度决策系统中测试代价敏感的属性与尺度同步选择

属性与尺度同步选择方法可有效解决涉及代价因素的多尺度决策系统的知识约简问题,然而在现有研究中,少有基于代价进行属性与尺度的同步选择,并且大多数算法只针对协调的多尺度决策系统或不协调的多尺度决策系统.为了解决这一问题,文中以最小化数据处理的总测试代价为目标,提出测试代价敏感的属性与尺度同步选择算法,同时适用于协调的多尺度决策系统和不协调的多尺度决策系统.首先,构造基于粗糙集的理论模型,模型中的概念及性质同时考虑属性因素和尺度因素.其次,基于粗糙集的理论模型,设计启发式算法,能基于测试代价对多尺度决策系统同时进行属性约简与尺度选择,并且不同的属性可选择不同的尺度.最后,在12个数据集上的实验验证文中算法的有效性、实用性及优越性.

多Agent系统中基于Rough集的推理

定义了多 Agent系统中的推理模型 .建立了在该模型下的 Rough集和基于 Rough集方法的 Rough包含计算或称集合连接计算 .在知识发现和数据挖掘中 ,集合之间往往不是给出它们的相等性 ,而是讨论它们之间的Rough包含或连接 .因为在不同的 Agent中集合之间关系的精确和一致解释往往是不容易获得的 .一般说来 ,一条基于决策表上的规则 ,满足前提公式个体的集合包含于满足结论公式个体的集合常常是用一种支持值和信赖系数来说明 Rough包含或连接的程度 .在分布式环境中多 Agent的情况下计算 Rough包含或连接的程度常常要求提供一个多 Agent之间互学习的界面 ,所以定义了一个多 Agent系统下的推理模型或称近似空间 ,它就是被用作互学习和计算的界面 ,因此在界面上定义了一种合成运算和包含程度计算 .从而可以实现在分布式环境下 ,由多 A-gent互相协作完成可定义集合的近似和集合之间的 Rough包含或连接程度的计算 .最后 ,通过实例说明了在分布式环境下 ,多

区间值直觉模糊β覆盖粗糙集模型

在新的β覆盖邻域系统上提出四种区间值直觉模糊β覆盖粗糙集模型,用于有效处理区间值直觉模糊信息的多属性决策问题.首先,从论域的区间值直觉模糊β覆盖出发,引入两类新的邻域系统并构造了四种不同类型的区间值直觉模糊β覆盖粗糙集,扩展了现有模型的适用范围.其次,深入研究了每种模型的数学性质,构建了所提出的四种模型之间的关联关系,为模糊β覆盖粗糙集领域的研究提供了理论基础.最后,为解决区间值直觉模糊信息的多属性决策问题,设计了决策算法并进行应用实例分析,并通过与其他决策方法的对比分析,表明了区间值直觉模糊β覆盖粗糙集在多属性决策问题中具有可行性和有效性.研究成果对于复杂模糊信息决策具有一定的参考和指导意义,并为解决多属性决策问题提供了新的思路和方法.

多粒度粒球粗糙集模型

基于粒球计算的粗糙集理论作为知识发现和数据挖掘的重要工具之一,已成功地应用于标记预测、属性约简等。而现有的粒球粗糙集模型仅仅是从单粒度出发,无法从多粒度角度对数据进行分析和处理,实际生活中仍有很多应用场景需从多粒度角度进行思考。将粒球计算思想结合到多粒度粗糙集模型,提出了多粒度粒球粗糙集模型,并讨论了该模型的相关性质。该模型通过纯度的设定对数据进行粒球划分,能够有效地刻画数据之间的内在联系,以此设计多粒度粒球粗糙集的正域生成算法。实验分析表明该模型的可行性和有效性。