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.     

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.     

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.     

基于Vague粗糙集信息熵的属性约简算法

针对复杂系统分析中的数据信息冗余问题,提出一种基于Vague粗糙集信息熵的属性约简算法。首先,对Vague粗糙集相关概念进行拓展,提出Vague粗糙集的扩展信息熵和广义信息熵的模型;其次,对基于信息熵的属性重要性度量和属性约简原理进行研究,进而提出了一种基于Vague粗糙集信息熵的监督式属性约简算法;最后,选取UCI数据库对算法性能进行验证,计算结果表明该算法实用有效。     

LSI潜在语义信息检索模型

本文介绍了基于向量空间的信息检索方法 ,检索词和文件之间的关系表示成一个矩阵 ,查寻信息表示为检索词权重的向量 ,通过求查寻和文件向量的夹角余弦确定出数据库中的相关文件 .使用矩阵的 QR分解和奇异值分解 ( SVD)来处理数据库本身的不确定性 ,本文的目的是说明线性代数中的基本概念可以很好解决信息检索 ( IR)问题     

知识库的精细关系比较

比较知识库精细关系的两种不同定义,得出等价关系与划分、二元关系与覆盖、以及二元关系与覆盖约简之间的一些有趣联系;进而探讨这两种定义在粗糙集中对提高知识确定性程度的不同作用.这些结论将对基于粗糙集的不确定性研究提供一定的帮助.     

基于粗糙集与模糊集的信息检索算法

结合粗糙集与模糊集理论,提出一种信息检索的新方法.该方法首先对已知文本信息按关键词进行模糊聚类;然后利用粗糙集理论求出各关键词的重要性程度;最后根据最大相似度原则对文本信息进行检索,若结果集中文本数量较大,则按文本与已知文本的相似度从高到低进行排序,先返回相似度较高的相关文档.     

广义模糊粗糙集的不确定性度量

研究广义模糊粗糙集的不确定性问题,利用一种新的信息熵定义模糊粗糙集的模糊性度量,并给出这种度量的性质,证明当且仅当A是经典可定义集合时其模糊粗糙集的模糊性度量FR(A)等于0。     

由KM-模糊伪度量诱导的模糊化粗糙集及其性质

本文首先介绍了由KM-模糊伪度量诱导的模糊等价关系。基于此模糊等价关系，然后，得到了由KM-模糊伪度量诱导的模糊化粗糙集，并研究了其性质。最后，详细探究了KM-模糊伪度量及其诱导的模糊化粗糙集之间的关系，丰富和完善了模糊粗糙集理论的研究内容。     

一种广义多粒度变精度粗糙集北大核心

本文基于文献[4]提出的广义多粒度粗糙集进行模型推广,提出广义多粒度变精度粗糙集模型.在多粒度的粒度不确定性的基础上考虑类选择的不确定性,研究新模型的一些基本性质并以实例计算说明.本文给出的广义多粒度变精度粗糙集为多粒度粗糙集理论的研究和应用奠定一定的理论基础.     

Information granulation and uncertainty measures in interval-valued intuitionistic fuzzy information systems

Information granulation and entropy are main approaches for investigating the uncertainty of information systems, which have been widely employed in many practical domains. In this paper, information granulation and uncertainty measures for interval-valued intuitionistic fuzzy binary granular structures are addressed. First, we propose the representation of interval-valued intuitionistic fuzzy information granules and examine some operations of interval-valued intuitionistic fuzzy granular structures. Second, the interval-valued intuitionistic fuzzy information granularity is introduced to depict the distinguishment ability of an interval-valued intuitionistic fuzzy granular structure (IIFGS), which is a natural extension of fuzzy information granularity. Third, we discuss how to scale the uncertainty of an IIFGS using the extended information entropy and the uncertainty among interval-valued intuitionistic fuzzy granular structures using the expanded mutual information derived from the presented intuitionistic fuzzy information entropy. Fourth, we discovery the relationship between the developed interval-valued intuitionistic fuzzy information entropy and the intuitionistic fuzzy information granularity presented in this paper.     

信息熵公理及其在决策分析中的应用

针对信息量是消息发生前的不确定性给出一个直观测量信息量公式.为了克服Shannon熵的局限性和分析信息度量本质,借鉴距离空间理论中度量公理定义的思路,通过非负性、对称性、次可加和极大性给出信息熵的公理化新定义.将Shannon熵、直观信息熵和β-熵等不同形式的信息度量统一在同一公理化结构下.应用直观信息熵公式仅采用四则运算进行决策树分析,避免了利用Shannon熵公式的对数运算.     

信息熵度量风险的探究

本文分析了风险的本质后指出,风险是某一特定行为主体对某一金融投资中损失的不确定性和收益的不确定性的认识。在众多风险度量的方法中,熵函数法有着其独特的度量风险的优势,因此,在本文中重点讨论了熵函数作为风险度量的合理性。同时提出一个新的风险度量模型,剖析其主要的数学特性,阐明该模型可以针对不同行为主体能有效地度量金融风险,并且计算量小,易于操作。     

Information theoretic measures of UHG graphs with low computational complexity

We introduce a novel graph class we call universal hierarchical graphs (UHG) whose topology can be found numerously in problems representing, e.g., temporal, spacial or general process structures of systems. For this graph class we show, that we can naturally assign two probability distributions, for nodes and for edges, which lead us directly to the definition of the entropy and joint entropy and, hence, mutual information establishing an information theory for this graph class. Furthermore, we provide some results under which conditions these constraint probability distributions maximize the corresponding entropy. Also, we demonstrate that these entropic measures can be computed efficiently which is a prerequisite for every large scale practical application and show some numerical examples.     

基于Vague集信息的多属性群决策专家水平评判方法

针对基于Vague集信息的多属性群决策专家水平评判问题提出了两种评判方法.首先引进了基于Vague集信息的多属性群决策信息体(即决策信息体)的相关概念,通过决策信息体构造了基于Vague集信息的一致性决策矩阵及模糊熵,其次利用Vague集信息的相似度量以及Vague集信息的模糊熵两种信息不确定性度量方法,对基于Vague集信息的多属性群决策专家水平评判问题提出了两种评判方法,即统计分析方法和模糊熵分析方法,对专家的评判水平进行排序.最后,通过一个算例说明两种方法的一致性、有效性和实用性.     

推理规则的信息熵描述

根据信息理论的一些基本观点首次定义了决策逻辑系统中公式的信息熵,由此给出了知识系统中推理规则信息熵的定义。然后讨论了推理规则信息熵的某些性质。随后又建立了一些推理规则信息熵有关的若干重要概念,从而揭示了信息论与知识表达系统之间的某些联系,为信息理论应用于人工智能及数据挖掘提供了一定的理论或技术性工具。     

Single valued neutrosophic cross-entropy for multicriteria decision making problems

A single valued neutrosophic set (SVNS) is an instance of a neutrosophic set, which give us an additional possibility to represent uncertainty, imprecise, incomplete, and inconsistent information which exist in real world. It would be more suitable to apply indeterminate information and inconsistent information measures. In this paper, the cross entropy of SVNSs, called single valued neutrosophic cross entropy, is proposed as an extension of the cross entropy of fuzzy sets. Then, a multicriteria decision-making method based on the proposed single valued neutrosophic cross entropy is established in which criteria values for alternatives are SVNSs. In decision making process, we utilize the single-valued neutrosophic weighted cross entropy between the ideal alternative and an alternative to rank the alternatives corresponding to the cross entropy values and to select the most desirable one(s). Finally, a practical example of the choosing problem of suppliers is provided to illustrate the application of the developed approach.     

模糊熵与距离测度的相互诱导及其应用

模糊信息论就是利用模糊数学这一工具来研究带有模糊不确定性的信息的．模糊熵和距离测度是模糊信息论中两个重要的度量方法．本文主要讨论模糊熵和距离测度之间的相互关系,由此得到几个由模糊熵诱导的距离测度公式和几个由距离测度诱导出的模糊熵公式,说明了模糊熵和距离测度是可以相互诱导的．最后,举例说明距离测度公式在模式识别中的应用．     

On the Measurement of Information in the Field of Criminal Detection

A method for measuring the value of information in those fields where the meaning of messages is important is discussed. At present there exists no accepted measure of information in such problem areas, the classical unit of bits flowing per second being unacceptable. It is shown that where two sets of phenomena are associated in some way with a given set of probabilities, e.g. a population and the crimes which are committed within it, the problem reduces to how much “choice” exists. This enables the concept of entropy to be used to advantage.After developing a measure of the value of information in the general case, the paper applies the method to the investigation of a case of simple larceny. This case illustrates several interesting features. Perhaps the most important feature is that some pieces of information, although they are very important to the police, only change the level of uncertainty very slightly. In such cases the piece of information generally demands that the police perform an action which either may provide useful information or lead to further action. To overcome this problem a potential entropy change is defined which takes this factor into account.It is hoped that the work may lead to a fuller understanding of how information flows in the police network. Thus it may be possible to see if the right information is getting into the police system, getting lost inside it or whether it is being used most efficiently.     

A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM) is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1) In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index) of National Stock Exchange of India (NSE) is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.