不协调决策信息系统的约简

知识约简是粗糙集理论中的一个重要内容,目前大多数研究都集中在协调的决策信息系统上。但现实中存在大量的不协调决策信息系统,针对该类系统的约简研究,将更具有实践意义。定义了决策包含度约简和最大决策包含度约简的概念,讨论了决策包含度约简和最大决策包含度约简的关系,即最大决策包含度约简弱于决策包含度约简,为解决不协调决策信息系统的知识约简问题提供了新方法。     

优势-等价关系下随机目标信息系统的属性约简

以证据理论中的信任测度和似然测度为基本工具,研究了优势关系下随机信息系统的属性约简问题,引入优势-等价关系下不协调随机目标信息系统的概念,并讨论了其上的分布约简,最后给出实例验证了约简方法的有效性。     

基于集值决策属性的集值信息系统

定义了决策属性也是集合子集的集值决策信息系统,给出了基于集值决策属性的协调集值决策信息系统的定义,得到了协调集值决策信息系统属性约简的判定定理和辨识矩阵,并讨论了在属性约简中起不同作用的属性分类及其特征。定义了不协调集值决策信息系统的分配协调集,给出了基于集值决策属性的不协调集值决策信息系统属性约简的方法。     

图支配集问题的粗糙集属性约简方法

探讨粗糙集的属性约简和图的支配集问题之间的联系。通过构造信息系统,将粗糙集的属性约简问题与图的支配集问题相联系,从而把图的支配集问题转化为粗糙集的属性约简问题。首先证明图的极小支配集恰是其构造的信息系统的属性约简,然后提出一种基于信息熵的最小支配集算法,最后通过实例验证该算法的可行性和有效性。     

属性合成区间值信息系统的0-1形式背景研究

区间值信息系统是常见的信息系统之一。针对相同论域上的区间值信息系统给出属性合成的区间值信息系统;将形式背景的基本理论引入到合成区间值信息系统中,讨论利用子形式背景直接构造原形式背景;进而研究子形式背景与原形式背景下协调集之间的关系,给出原形式背景下同构概念格的构造方法;最后利用实例验证相关结论。     

软集参数约简的新方法

软集理论是一种新的处理不确定性问题的数学工具. 讨论了软集与信息系统之间的关系, 介绍了由软集诱导的二元关系, 借助信息系统的属性约简来研究软集的参数约简, 得到了软集参数约简的新方法, 并通过算例验证了方法的有效性.

     

不协调格值目标信息系统的近似约简

针对不协调格值目标信息系统的属性约简问题,提出不协调格值目标信息系统上近似约简与下近似约简的概念,并得到2种约简的判定定理。给出求解上、下近似约简的辨识矩阵及约简方法。通过实例验证得出,该约简方法具有实效性,格值目标信息系统的值域最为广泛,适用于由属性值域构成格的信息系统     

集值决策信息系统的知识约简与属性特征

本文定义了集值决策信息系统中的一种新的关系,给出了在这种关系下协调集值决策信息系统属性约简的判定定理和辨识矩阵,从而得到了知识约简的具体操作方法,并讨论了在属性约简中起不同作用的属性分类及其特征。定义了不协调集值决策信息系统的分配协调集,给出了一种将不协调集值决策信息系统转化为广义协调近似空间的方法,并证明了不协调集值决策信息系统的分配协调集就是广义协调近似表示空间的协调集。     

集值信息系统基于变精度相容关系的知识约简

借助于属性集值的相似程度在集值信息系统上定义了一种变精度相容关系,给出了这种变精度相容关系下目标协调集值信息系统的知识约简与判定,并讨论了相容水平对目标协调集值信息系统的知识约简的影响。     

基于闭算子的属性约简

对于协调的信息系统,定义了其条件属性集的幂集上的两个闭算子C(R)与C(r),讨论了相应闭集族的性质,并证明了它们与不可辨识属性集族之间的关系。提出属性约简的一种新方法,给出Cr=CR的充要条件,并证明所定义的属性约简与文献[4,7]中约简的等价性。     

基于随机集的合成信息系统粗集模型

研究基于随机集的实值信息系统及其合成系统的粗糙集模型。用随机集值映射表示专家对实值信息系统知识结构的认识,给出 2类合成信息系统的形式化定义,并研究对象合成信息系统和属性合成信息系统的基于随机集值映射的粗糙集模型及其性质,讨论其中上、下近似算子之间的关系。实例证明,该模型能利用此关系由原信息系统模型估算或计算出其合成信息系统模型的上、下近似算子。     

Rough reduction in algebra view and information view

Rough set (RS) is a valid theory to deal with imprecise, uncertain, and vague information. It has been applied successfully since it was developed by Professor Z. Pawlak in 1982 in such fields as machine learning, data mining, intelligent data analyzing, control algorithm acquiring, etc. The greatest advantage of the RS is its great ability to compute the reductions of information systems. Many researchers have done a lot of work in developing efficient algorithms to compute useful reductions of information systems. There also are some researchers working on the relationship between rough entropy and information entropy. They have developed some efficient reduction algorithms based on conditional information entropy. In this article, the relationship of the definitions of rough reduction in algebra view and information view is studied. Some relationships such as inclusion relationship under some conditions and equivalence relationship under some other conditions are presented. The inclusion relationship between the attribute importance defined in algebra view and information view is presented also. Some efficient heuristic reduction algorithms can be developed further using these results. © 2003 Wiley Periodicals, Inc.     

大变量多输出逻辑函数实质项识别算法

在对Espresso算法进行分析改进的基础上，提出了一种基于全域识别的多输入多输出逻辑函数实质本源项、完全冗余项和相对冗余项生成算法，该算法通过对基于积项表示的多输入多输出逻辑函数的余因子计算来进行全域判断，根据全域判断结果来识别实质本源项、完全冗余项和相对冗余项，从而构成实质本源项集合、完全冗余项集合和相对冗余项集合。对基于二级SOP型的多输入多输出逻辑函数设计了多输入多输出逻辑函数优化识别软件系统，允许的最大输入变量数为128、最大输出变量数为256、最大输入输出变量总和为300、最大输入积项数为20 000。软件系统在Pentium 1.8GHz、512MB内存的计算机上通过了Benchmark例题的测试。     

合成信息系统与子信息系统

本文给出了对象合成信息系统、属性合成信息系统、对象子信息系统及属性子信息系统的定义，分别讨论了它们的上下近似算子与原信息系统的上下近似算子之间的关系．并给出了它们的一些实际应用。     

合成信息系统与子信息系统的属性特征

数据库的合成和分解在实际应用中是一个很重要的问题,它所对应的数学模型就是信息系统的合成与分解,该文给出了对象合成信息系统、属性合成信息系统、对象子信息系统及属性子信息系统的定义,讨论了合成信息系统、子信息系统与原信息系统等属性特征之间的关系。     

Model reduction of uncertain systems retaining the uncertainty structure

Model reduction of high order linear-in-parameters discrete-time systems is considered. The main novelty of the paper is that the coefficients of the original system model are assumed to be known only within given intervals, and the coefficients of the derived reduced order model are also obtained in intervals, such that the complex value sets of the uncertain original and reduced models will be optimally close to each other on the unit circle. The issue of inclusion of one value set in another is also addressed in the paper. The meaning of model reduction is defined for linear-in-parameters systems. The algorithm for obtaining the value sets of such systems is derived in the paper. Then, applying a novel approach, the infinity norm of “distance” between two polygons representing the original and the reduced uncertain systems is minimized. A noteworthy point is that by a special definition of this distance the problem is formulated as a linear semi-infinite programming problem with linear constraints, thus reducing significantly the computational complexity. Numerical example is provided.     

Transfer dimensionality reduction by Gaussian process in parallel

Dimensionality reduction has been considered as one of the most significant tools for data analysis. In general, supervised information is helpful for dimensionality reduction. However, in typical real applications, supervised information in multiple source tasks may be available, while the data of the target task are unlabeled. An interesting problem of how to guide the dimensionality reduction for the unlabeled target data by exploiting useful knowledge, such as label information, from multiple source tasks arises in such a scenario. In this paper, we propose a new method for dimensionality reduction in the transfer learning setting. Unlike traditional paradigms where the useful knowledge from multiple source tasks is transferred through distance metric, we attempt to learn a more informative mapping function between the original data and the reduced data by Gaussian process that behaves more appropriately than other parametric regression methods due to its less parametric characteristic. In our proposal, we firstly convert the dimensionality reduction problem into integral regression problems in parallel. Gaussian process is then employed to learn the underlying relationship between the original data and the reduced data. Such a relationship can be appropriately transferred to the target task by exploiting the prediction ability of the Gaussian process model and inventing different kinds of regularizers. Extensive experiments on both synthetic and real data sets show the effectiveness of our method.     

邻域信息系统基于同态的数据压缩

用同态的概念作为处理部域信息系统中数据压缩的工具。给出了部域信息系统和诱导的部域信息系统概 念,并在此基础上定义了一种新的协调函数,研究了在同态映射下部域信息系统的一些重要性质,证明了原部域信息 系统与同态象部域信息系统约简的等价性,从而得到一种通过计算数据量较少的象信息系统的约简来对原系统进行 数据压缩的方法.     

Some improved results on communication between information systems

To study the communication between information systems, Wang et al. [C. Wang, C. Wu, D. Chen, Q. Hu, C. Wu, Communicating between information systems, Information Sciences 178 (2008) 3228-3239] proposed two concepts of type-1 and type-2 consistent functions. Some properties of such functions and induced relation mappings have been investigated there. In this paper, we provide an improvement of the aforementioned work by disclosing the symmetric relationship between type-1 and type-2 consistent functions. We present more properties of consistent functions and induced relation mappings and improve upon several deficient assertions in the original work. In particular, we unify and extend type-1 and type-2 consistent functions into the so-called neighborhood-consistent functions. This provides a convenient means for studying the communication between information systems based on various neighborhoods.