基于交叉熵的ITFN的多属性决策方法

针对属性值为直觉梯形模糊数且属性权重完全未知的多属性决策问题,提出了一种基于交叉熵的决策方法。给出期望值的方法将直觉梯形模糊数转化为直觉模糊数,进而提出直觉模糊数的交叉熵等概念及相关性质。基于各方案与正理想方案的总区别信息最小化原则,建立非线性模型,求出属性权重。用实例说明该方法的有效性。     

一类区间粗糙数型多属性决策方法研究

研究属性值为区间粗糙数,具有属性优先序信息的多属性决策问题.首先给出粗糙属性值的规范化公式;然后基于区间粗糙数的相离度定义,建立以极大化属性值的离差为目标的最优化模型来确定各属性权重;进而基于粗糙算术运算以及期望值算子,计算出各方案的期望效用值,从而获得各方案的排序;最后通过数值例子表明了所提出方法的有效性.     

多属性决策的模糊理想点法

讨论了属性值为三角模糊变量的多属性决策问题,提出了确定模糊正理想点和模糊负理想点的方法,给出了基于模糊正理想点和模糊负理想点对各方案进行排序的方法。给出算例验证了所提出的方法的有效性,并验证了不同方法所确定的模糊正理想点和模糊负理想点,会导致方案排序的改变。     

广义犹豫正态模糊信息集成及其多属性群决策

定义了犹豫正态模糊元及其运算法则、得分函数、Euclidean距离等概念;提出了广义犹豫正态模糊有序加权平均算子,并研究其性质,该算子不仅尽可能多地保留决策者的偏好信息,还可依据决策者的主观意愿选择不同的参数和属性权重,使得决策结果达到决策者的期望值;紧接着对属性权重和算子参数赋予不同的数值,获取广义犹豫正态模糊有序加权平均算子的若干种特殊算子,并探讨两个常用算子的大小关系;针对属性权重完全未知的多属性群决策问题,构建一种基于广义犹豫正态模糊有序加权平均算子的群决策方法。该方法利用同一属性下所有方案属性值间的距离求得最优权重,然后将同一方案下各属性值集结成为综合属性值,进而得到方案优劣排序。通过实例分析说明该方法的可行性和有效性。     

基于投影技术的三角模糊数型多属性决策方法研究

针对属性权重完全未知且属性值为三角模糊数的多属性决策问题.提出一种基于线性规划和模糊向量投影的决策方法.该方法基于加权属性值离差最大化建立一个线性规划模型,通过求解此模型得到属性的权重,计算各方案的加权属性值在模糊正理想点和负理想点上的投影,进而计算相对贴近度,并据此对方案进行排序,最后,通过算例说明了模型及方法的可行性和有效性.     

三角模糊数在多属性决策中的建模

为了获得三角模糊数的多属性决策数学模型,将语言变量和三角模糊数引入到多属性决策中,用语言变量或三角模糊数表示多属性决策中的属性值和各属性的权重,采用加权和法求得每个备选方案的三角模糊数最终评价值,并应用三角模糊数的减法运算定义任意两个三角模糊数的优越度,在方案两两对比求得优越度后建立方案的互补判断矩阵,采用最小平方法求得方案排序向量,根据排序向量元素的大小进行方案排序。     

基于直觉模糊云模型的TOPSIS多属性决策方法

针对属性值为直觉模糊数的多属性决策问题,提出了一种基于直觉模糊云模型的TOPSIS多属性决策方法。首先,利用直觉模糊云对备选方案的各个属性值进行描述,计算其数字特征——期望、熵和超熵;然后,构造各数字特征的决策矩阵,获得其对应的正、负理想解;最后,计算各数字特征与正、负理想解间的距离,进而获得综合贴近度,对备选方案进行排序,获得最优决策结果,并通过具体数值实例验证方法的合理性和有效性。     

一类基于指数型模糊数的模糊多属性topsis决策

针对指数型模糊数上的模糊多属性决策问题,根据模糊理想点法的思想,给出两种多属性topsis决策方法。通过定义指数型模糊数的期望值,实现属性权重向量的解模糊化处理;定义指数型模糊数之间的距离测度,以计算各方案与理想方案之间的距离。基于期望值和距离测度的定义,从两种不同的角度出发,给出了两种模糊多属性topsis决策方法。实例验证两种方法的可行性和有效性,并对这两种方法进行比较和分析。     

一种基于前景理论的不确定语言变量风险型多属性决策方法

针对区间概率条件下属性值为不确定语言变量的风险型多属性决策问题,提出了一种基于前景理论的决策方法．首先建立了不确定语言变量的前景价值函数和区间概率权重函数,计算了方案在各属性下的前景值;然后通过属性权重得到各方案的加权前景值,利用各方案加权前景值的期望值对方案进行排序;最后通过应用案例说明了该方法的决策步骤和价值函数不...     

基于云模型的Pythagorean模糊语言多属性决策方法

针对专家给出的属性值为Pythagorean模糊语言且专家权重与属性权重均未知的多属性决策问题进行了研究,提出一种基于云模型的多属性决策方法。首先,根据Pythagorean模糊语言决策信息的距离熵计算得到属性权重;其次,计算决策矩阵间的距离从而得到各决策专家权重;再次,构建Pythagorean模糊云模型决策矩阵并利用专家权重和属性权重进行信息集结;最后,基于TOPSIS方法求取正、负理想解,依据理想解计算各方案贴近度并据此对各备选方案进行排序选择。案例分析表明,该方法优化了复杂环境下的决策,避免了决策信息的丢失,能够较好解决决策信息的不确定性和决策过程的随机性,具有一定的可行性和有效性。     

基于属性重要度的风险决策粗糙集属性约简

基于Pawlak 粗糙集的属性约简一般保持决策表的正区域不变, 然而由于现实中不同用户对不同约简精度的需求, 获取属性值的实际代价与个人偏好可能不同. 针对决策者主观个人偏好、客观约简精度、获取属性值的实际代价和决策表各区域的误判代价等综合情况, 提出新的约简算法, 并讨论约简代价与约简精度间的关系. 通过遗传算法, 采用启发式方法搜索出局部最优约简子集. 仿真实验表明, 所提出的算法操作性强, 更适合处理实际决策问题.

     

一种高效属性可撤销的属性基加密方案

密文策略的属性基加密(CP-ABE)可以实现数据拥有者定义的基于外挂加密数据的访问控制,使它在数据分享细粒度访问控制中有着良好的应用前景,然而在实际应用系统中仍然存在属性撤销方面急需解决的问题.在代理重加密技术和CP-ABE技术相结合的方案基础上,引入Shamir的秘密分享技术和树访问结构,实现了门限运算和布尔运算的结合,并且缩短了密钥和密文长度.与之前方案相比,在效率和表达能力方面有了明显的提高,此外该方案在判定双线性假设下是安全的.     

k-visit attribute grammars

It is shown that any well-defined attribute grammar isk-visit for somek. Furthermore it is shown that given a well-defined grammarG and an integerk, it is decidable whetherG isk-visit. Finally we show that thek-visit grammars specify a proper hierarchy with respect to translations.     

粒计算中基于属性分类的形式概念属性约简

针对目前已有的形式概念属性约简算法的不足(如属性约简的时间复杂度偏高、属性及属性值比较过程中存在冗余计算、存储开销大等问题),结合粒计算思想,提出基于属性分类的形式概念属性约简模型.首先,通过定义两个算子来划分属性之间分类关系;然后,由属性分类关系制定约简规则,并在此基础上提出基于属性分类的形式概念约简算法,该算法在保持目前最低时间复杂度不变的情况下,减少了冗余计算和存储开销,提高了属性约简的计算效率;最后,通过实例和仿真实验对基于属性分类关系的形式概念属性约简算法的有效性进行了验证.     

综合属性选择和删除的属性约简方法

属性约简能有效地消除信息冗余,广泛应用于人工智能、机器学习.通过实例指出基于辨识矩阵的经典的属性约简方法存在不能得到约简的可能性,仍具有冗余性.因此,提出了综合属性选择和删除算法的辨识矩阵属性约简方法,并有效解决该问题.通过UCI标准数据集验证表明,新方法比经典方法进一步减少了属性的个数,凸显其实用性和有效性.     

Circular attribute grammars with remote attribute references and their evaluators

Attribute grammars (AGs) are a suitable formalism for the development of language processing systems. However, for languages including unrestricted labeled jumps, such as “goto” in C, the optimizers in compilers are difficult to write in AGs. This is due to two problems that few previous researchers could deal with simultaneously, i.e., references of attribute values on distant nodes and circularity in attribute dependency. This paper proposescircular remote attribute grammars (CRAGs), an extension of AGs that allows (1) direct relations between two distant attribute instances through pointers referring to other nodes in the derivation tree, and (2) circular dependencies, under certain conditions including those that arise from remote references. This extension gives AG programmers a natural means of describing language processors and programming environments for languages that include any type of jump structure. We also show a method of constructing an efficient evaluator for CRAGs called amostly static evaluator. The performance of the proposed evaluator has been measured and compared with dynamic and static evaluators. Akira Sasaki: He is a research fellow of the Advanced Clinical Research Center in the Institute of Medical Science at the University of Tokyo. He received his BSc and MSc from Tokyo Institute of Technology, Japan, in 1994 and 1996, respectively. His research interests include programming languages, programming language processors and programming environments, especially compiler compilers, attribute grammars and systematic debugging. He is a member of the Japan Society for Software Science and Technology. Masataka Sassa, D.Sc.: He is Professor of Computer Science at Tokyo Institute of Technology. He received his BSc, MSc and DSc from the University of Tokyo, Japan, in 1970, 1972 and 1978, respectively. His research interests include programming languages, programming language processors and programming environments, currently he is focusing on compiler optimization, compiler infrastructure, attribute grammars and systematic debugging. He is a member of the ACM, IEEE Computer Society, Japan Society for Software Science and Technology, and Information Processing Society of Japan.     

Test-cost-sensitive attribute reduction

In many data mining and machine learning applications, there are two objectives in the task of classification; one is decreasing the test cost, the other is improving the classification accuracy. Most existing research work focuses on the latter, with attribute reduction serving as an optional pre-processing stage to remove redundant attributes. In this paper, we point out that when tests must be undertaken in parallel, attribute reduction is mandatory in dealing with the former objective. With this in mind, we posit the minimal test cost reduct problem which constitutes a new, but more general, difficulty than the classical reduct problem. We also define three metrics to evaluate the performance of reduction algorithms from a statistical viewpoint. A framework for a heuristic algorithm is proposed to deal with the new problem; specifically, an information gain-based λ-weighted reduction algorithm is designed, where weights are decided by test costs and a non-positive exponent λ, which is the only parameter set by the user. The algorithm is tested with three representative test cost distributions on four UCI (University of California - Irvine) datasets. Experimental results show that there is a trade-off while setting λ, and a competition approach can improve the quality of the result significantly. This study suggests potential application areas and new research trends concerning attribute reduction.     

Inductive attribute grammars

Attribute grammars are traditionally constrained to be noncircular. In using attribute grammars to specify the semantics of programming languages, this noncircularity limitation has restricted attribute grammars to compile-time or static semantics. Inductive attribute grammars add a general form of circularity to this standard approach. Inductive attribute grammars have the expressiveness required to describe the full semantics of programming languages, while at the same time maintaining the declarative character of standard attribute grammars. This expanded view of attribute grammars proves to be useful in interactive language-based programming environments, as inductive attribute grammars allow the environment to provide an interpreter for incremental re-evaluation of programs after small changes to the code. The addition of run-time semantics via circular attribute grammars permits automatically generated environments to be complete, in that incremental static semantic checking and fast incremental execution are now available within a single framework.The authors' present affiliations are the United States Geological Survey and the Northrop Corporation respectively.     

Guarded attribute grammars

Contrary to a widely-held belief, it is possible to construct executable specifications of language processors that use a top-down parsing strategy and which have structures that directly reflect the structure of grammars containing left-recursive productions. A novel technique has been discovered by which the non-termination that would otherwise occur is avoided by ‘guarding’ top-down left-recursive language processors by non-left-recursive recognizers. The use of a top-down parsing strategy increases modularity and the use of left-recursive productions facilitates specification of semantic equations. A combination of the two is of significant practical value because it results in modular and expressively clear executable specifications of language processors. The new approach has been tested in an attribute grammar programming environment that has been used in a number of projects including the development of natural language interfaces, SQL processors and circuit design transformers within a VLSI design package.     

The effect of attribute normalization factors in attribute distance weighted average

Attribute distance weighted average (ADWA) is a new filtering paradigm, which can progressively alleviate the denoising contradiction between the noise removal and feature preservation by introducing new attributes. As the key control parameters in ADWA, the attribute normalization factors play an important role in the final filtering result. An in-depth study is necessary to exam the effect the attribute normalization factors have on the filtering performance and the rules they follow, which can then serve as a guide for the determination and optimization of attribute normalization factors. For this purpose, the three attributes of a signal, “Location,” “Value,” and “Gradient,” are studied as an example in this paper. Experimental results indicate that the normalization factors directly determine the strength of the effect the corresponding attributes have on the filtering result. If the normalization factor increases, ADWA’s ability in noise removal becomes stronger and meanwhile its ability in feature preservation becomes weaker. Therefore, the denoising contradiction still exists for ADWA of a specific attribute rank. However, since different attributes contribute to the filtering performance independently in different regions of a signal, the denoising contradiction can be further alleviated by introducing new attributes, and thus a more satisfactory outcome can be obtained.