水雾化粉末质量预报系统的建模与知识获取

水雾化粉末质量预报系统具有原始数据不全的特点,是一个不完备的信息系统.本文依据此特点,结合变精度粗糙集方法,在相客关系的基础上引入集对分析方法,提出一种基于集对势容差关系的扩充粗糙集方法,并应用于该信息系统的建模中,通过对α的调节和控制,有效保证了制粉生产条件属性容差类划分的准确性和灵活性.最后,本文以该模型为基础,使用基于属性重要性的贪心算法讨论了制粉生产条件属性约简和知识获取的方法、步骤和决策规则.经实际生产验证了获取的预报知识的合理性和有效性,对制粉工艺和生产真正起到了优化作用.     

基于集对分析下的变精度Bayesian粗糙集模型

引入集对分析概念,提出了一种基于集对分析下的变精度Bayesian粗糙集模型,这就将经典粗糙集模型和变精度Bayesian粗糙集模型推广到了不完备信息系统,并且得到了该模型的上、下近似的一些性质.最后,给出了与该模型定义等价的一个定理.     

基于覆盖的概率粗糙集模型及其Bayes决策

经典的Pawlak概率粗糙集模型是基于论域上的等价关系而建立的,然而在实际应用中等价关系很难得到.因此,许多学者建立了基于一般关系(如容差关系、相似关系等)的Pawlak粗糙集模型.本文建立了基于覆盖关系的概率粗糙集模型,推广和总结了前人的工作.同时,提出了该模型下的Bayes决策方法和应用实例.     

不完备信息系统中近似算子研究

近年来出现了许多用粗糙集理论处理各种信息系统的方法,其中对不完备信息系统的研究一直是个热点。本文采用Grzymala-Busse提出的刻画关系研究"丢失"和"暂缺"两种语意并存的不完备信息系统,考察了点近似、子集近似和概念近似算子的性质,并且给出这些上、下近似之间关于包含关系的序关系,为进一步研究不完备信息系统打下理论基础。     

不完备直觉模糊信息系统属性约简新方法

为了解决不完备直觉模糊(决策)信息系统属性约简问题,首先利用属性值贴近度和阈值λ构建不完备直觉模糊信息系统的广义λ-容差关系,然后利用此广义λ-容差关系来定义不完备直觉模糊(决策)信息系统的广义λ-可辨识矩阵,再后根据广义λ-可辨识矩阵构造不完备直觉模糊(决策)信息系统的广义λ-辨识公式,最后通过所构造的广义λ-辨识公式的极小析取范式可以唯一确定不完备直觉模糊(决策)信息系统所有(相对)约简。通过算例说明所介绍的属性约简方法的科学性和有效性。     

基于黄金关系的粗糙集模型及其应用

粗糙集的核心概念是基于等价关系的上、下近似集.给出了基于黄金关系的粗糙集模型,并研究了该模型的实际应用.     

均值限制相似优势粗糙集及决策分析模型研究

针对不完备信息系统中的偏好多属性决策问题,提出了一种基于均值限制相似优势粗糙集的决策分析模型.首先提出了均值限制相似优势关系的概念;然后在均值限制相似优势关系下得到知识的粗糙近似和属性约简,给出了分类决策规则.与相似优势关系和限制相似优势关系比较研究的结果表明:均值限制优势关系的分类精度和质量介于二者之间,而分类误差率则优于相似优势关系和限制相似优势关系,得到的决策规则可信度更高,决策模型与实际情况更加相符.     

基于优势关系的不协调区间值目标信息系统的分配约简

区间值信息系统是单值信息系统的一种推广模型,知识约简是粗糙集理论的核心问题之一,在基于优势关系下的不协调区间值目标信息系统中引入了分配约简和近似约简的概念,分别讨论了它们二者之间的关系,进一步给出了知识约简的判断定理和辨识矩阵,从而提供了在优势关系下不协调区间值目标信息系统分配约简的具体方法。     

基于完备IMTL代数的L模糊粗糙集

IMTL代数是一类重要的非经典逻辑代数,基于IMTL代数的L模糊粗糙集可以刻画信息系统中具有不完备性、模糊性与不可比较性的信息.本文讨论了基于完备IMTL代数的L模糊粗糙集的表示定理,还讨论了此种L模糊粗糙集的上下近似算子的性质以及近似算子的公理化定义方法.     

集值决策表基于邻域关系的属性约简

集值信息系统是完备信息系统的广义形式,它当中的一些对象在某些属性下的取值可能不止一个,反映的是信息的不确定性.本文在集值信息系统上引入对象的邻域关系,并以每个对象的邻域作为基本集,建立了集值信息系统的粗糙集方法.为了简化的知识表示,我们进一步讨论了邻域协调集值决策表的正域约简与邻域不协调集值决策表的近似分布约简,给出了正域约简与近似分布约简的等价刻画条件,并借助区分函数给出了计算正域约简与近似分布约简的方法.     

Extended rough set model based on known same probability dominant valued tolerance relation

Classical rough set theory is based on the conventional indiscernibility relation. It is not suitable for analyzing incomplete information. Some successful extended rough set models based on different non-equivalence relations have been proposed. The data-driven valued tolerance relation is such a non-equivalence relation. However, the calculation method of tolerance degree has some limitations. In this paper, known same probability dominant valued tolerance relation is proposed to solve this problem. On this basis, an extended rough set model based on known same probability dominant valued tolerance relation is presented. Some properties of the new model are analyzed. In order to compare the classification performance of different generalized indiscernibility relations, based on the category utility function in cluster analysis, an incomplete category utility function is proposed, which can measure the classification performance of different generalized indiscernibility relations effectively. Experimental results show that the known same probability dominant valued tolerance relation can get better classification results than other generalized indiscernibility relations.     

基于双论域模糊粗糙集的应急决策模型与方法

以突发危机事件应急决策为应用背景,讨论了双论域上模糊粗糙集的基本理论,建立了基于模糊相容关系的双论域模糊粗糙集模型. 在此基础上,把突发危机事件应急决策转化为一个具有模糊决策对象的双论域决策近似空间上的粗糙近似问题,构建了基于双论域模糊粗糙集的应急决策模型.首先在双论域近似空间中计算模糊决策对象的上(下)近似,进而结合经典非确定型决策的思想给出了突发危机事件应急决策的规则.同时,给出了模型的算法.该模型给出了一种在不完全信息环境下应急决策的方法,给出了在充分考虑决策者个人偏好信息基础上的决策置信度以及最优决策规则.该方法能够比较充分地符合应急决策信息不充分、资源有限以及时间紧迫的基本特征, 进而对突发危机事件应急决策提供科学的理论基础和现实的决策方法.最后,通过应用算例说明了模型的应用过程,结果验证了本文给出模型的有效性。     

A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems

With the rapid growth of data sets nowadays, the object sets in an information system may evolve in time when new information arrives. In order to deal with the missing data and incomplete information in real decision problems, this paper presents a matrix based incremental approach in dynamic incomplete information systems. Three matrices (support matrix, accuracy matrix and coverage matrix) under four different extended relations (tolerance relation, similarity relation, limited tolerance relation and characteristic relation), are introduced to incomplete information systems for inducing knowledge dynamically. An illustration shows the procedure of the proposed method for knowledge updating. Extensive experimental evaluations on nine UCI datasets and a big dataset with millions of records validate the feasibility of our proposed approach.     

Updating attribute reduction in incomplete decision systems with the variation of attribute set

In rough set theory, attribute reduction is a challenging problem in the applications in which data with numbers of attributes available. Moreover, due to dynamic characteristics of data collection in decision systems, attribute reduction will change dynamically as attribute set in decision systems varies over time. How to carry out updating attribute reduction by utilizing previous information is an important task that can help to improve the efficiency of knowledge discovery. In view of that attribute reduction algorithms in incomplete decision systems with the variation of attribute set have not yet been discussed so far. This paper focuses on positive region-based attribute reduction algorithm to solve the attribute reduction problem efficiently in the incomplete decision systems with dynamically varying attribute set. We first introduce an incremental manner to calculate the new positive region and tolerance classes. Consequently, based on the calculated positive region and tolerance classes, the corresponding attribute reduction algorithms on how to compute new attribute reduct are put forward respectively when an attribute set is added into and deleted from the incomplete decision systems. Finally, numerical experiments conducted on different data sets from UCI validate the effectiveness and efficiency of the proposed algorithms in incomplete decision systems with the variation of attribute set.     

An interval set model for learning rules from incomplete information table

A novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the uncertain concepts. Interval set operations are discussed, and the connectives of concepts are represented by the operations on interval sets. Certain inclusion, possible inclusion, and weak inclusion relations between interval sets are presented, which are introduced to induce strong rules and weak rules from incomplete information table. The related properties of the inclusion relations are proved. It is concluded that the strong rules are always true whatever the missing values may be, while the weak rules may be true when missing values are replaced by some certain known values. Moreover, a confidence function is defined to evaluate the weak rule. The proposed approach presents a new view on rule induction from incomplete data based on interval set.     

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.     

知识库中知识的信息表示及其上的粗动力系统

粗集理论对知识进行了形式化定义,它为处理不确定,不完整的海量数据知识提供了一套严密的数据分析处理工具．但粗集概念及运算的代数意义表示往往不易被人理解．本文针对于此。在知识库中提出了知识的信息熵问题,证明了知识的某些信息表示与其代数表示是等价的,最后还讨论了知识库上的粗动力系统的一些性质。     

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.