基于多属性群决策的垃圾邮件过滤

提出一种基于多属性群决策的垃圾邮件过滤方法来解决垃圾邮件过滤器出现误判的情况,其基本思想是通过OWA(有序加权平均,ordered weighted averaging)算子和CWAA(组合加权平均,combination weighted arithmetic averaging)算子相结合的多属性群决策方法对每封邮件计算出一个合理的综合评价值作为隶属度,然后采用FSVM分类算法对邮件进行分类.实验表明该算法在一定程度上提高了垃圾邮件过滤的召回率和准确率.     

基于语言型混合算子的模糊信息聚合方法

在多属性决策过程中经常会用到聚合算子,有序加权平均聚合(OWA)算子是最常用的聚合算子之一,通常用于聚合确切的数值.然而,现实世界部分信息的不确定性以及决策者对一些信息的模糊性,使得部分信息不能用确切的数值表示,从而导致OWA算子及其扩展算子向着多元化发展.对此,给出一种语言型混合有序加权平均聚合(LHOWA)算子,同时研究该算子所应具备的一些基本性质,并给出一种基于该算子的语言型信息聚合方法,用于多属性决策过程中模糊信息的聚合.最后,通过一个煤矿安全评价的算例对所提出方法的优越性进行了验证.     

基于三支决策的模糊信息系统OWA算子参数选择

在模糊信息系统中，通过有序加权平均（Ordered weighted averaging,OWA）算子计算对象相似度，可以建立λ截集的相容关系。当λ的值确定后，OWA算子中量词参数(α,β)的选取直接关系到不可区分关系的建立以及信息粒的粗细。因此合理选取参数(α,β)是值得进一步研究的问题。本文采用粗糙集三支决策理论，研究模糊信息系统 OWA算子参数选择的相关内容。基于参数的激进、中庸和消极语义，确定OWA算子(α,β)的3种常用取值；进而研究相似度、相容类、双向近似和三支区域在3种参数选择下的性质关系。最后利用实例验证分析了模糊量词参数语义解释的合理性。本文采用三支决策创新视角，得到模糊信息系统OWA算子的深入性质，为相关模糊量词参数提供语义解释与选择依据。     

基于OWA算子的模糊n-cell数排序方法

针对基于模糊n-cell数的多属性排序问题,提出了一种基于有序加权平均算子（OWA算子）的模糊n-cell数排序方法。该方法首先根据样本数据对评估对象的属性构造模糊n-cell数,其次根据均值将属性按照从大到小排列,然后选取合适的权重向量,应用OWA算子进行信息聚合得到综合模糊n-cell数,接着根据各分量均值得到排序结果。最后,将该方法运用到实例中,并与传统的均值方法进行了比较。结果表明该方法不仅灵活有效,可根据具体情况选择不同的OWA权重来消除部分不合理的情况,使结果更有说服力,还弥补了传统均值方法的不足。     

基于贝叶斯算法和费舍尔算法的垃圾邮件过滤系统设计与实现

贝叶斯过滤算法和费舍尔过滤算法均是利用统计学知识对于垃圾邮件进行过滤的算法,有着良好的过滤效果。该文设计将某一词组(单词)出现概率使用加权计算的方法,改善了朴素贝叶斯算法和朴素费舍尔的邮件过滤算法对于出现较少的单词误判情况,使系统对于垃圾邮件判断的准确率上升。设计可以使用个性化的垃圾邮件过滤方案,支持使用邮件下载协议(POP3、IMAP协议)从邮件服务器下载邮件,以及使用邮件解析协议(MIME协议)对于邮件进行解析,支持邮件发送协议(SMTP协议)帮助用户发送邮件。     

基于OWA聚合算子的多传感器目标识别

将OWA算子引入到多传感器属性信息融合领域中来,建立了基于OWA算子的两级式多传感器目标识别推理模型。通过合理选择OWA算子中加权向量的与或度及各类传感器的属性权,有效地调整各传感器之间的融合程度,使整个目标识别系统的性能得到进一步提高。文末以该模型的目标识别实例表明了该方法的有效性和实用性。     

基于OWA算子的区间值加权模糊推理

针对如何对区间值模糊产生式规则赋予合理权值的问题,将OWA算子引入到区间值模糊推理中。介绍一种基于OWA算子的区间值赋权方法,根据此方法给出区间值模糊集上的加权模糊产生式规则的推理算法。在采用该算法的过程中,为合理地计算输入事实与规则前件的匹配程度,引入基于OWA算子的区间值模糊匹配函数值和总体贴近度的计算方法。实例分析表明了所给出的区间值模糊推理算法的有效性和可行性。     

基于粗糙集的带决策规则边界的邮件过滤算法

针对垃圾邮件过滤的准确率和稳定性不高,以及为了解决邮件过滤算法在语料分类上存在漏报和误报等问题,提出基于粗糙集的带决策规则边界的邮件过滤算法(RARM)。该算法运用粗糙集理论对语料库进行直接分析,并采用启发式方法提出了粗糙集理论的三种不同决策规则的执行计划,确保当邮件内容的词汇语义较为模糊时,仍能保证一定的分类准确度。在实验仿真中,通过与基于支持向量机(SVM)、Ada Boost和贝叶斯分类的邮件过滤算法相比较,该算法在垃圾邮件过滤上的准确率优于对比算法。     

结合OWA算子和模糊DEMATEL的风险评估方法

针对传统故障模式与影响分析(FMEA)方法在实际应用中的不足,提出一种基于有序加权平均(OWA)算子和决策试行与评价实验法(DEMATEL)的风险排序方法。FMEA专家对故障模式的3个风险因子给出模糊评价信息,应用OWA算子对评估信息进行集结,得到各故障原因对故障模式的影响强度。采用模糊DEMATEL法构建FMEA系统要素间的初始直接影响矩阵,经过运算可得综合影响矩阵,并计算各故障原因的原因度,据此进行产品或系统的失效风险评估。运用该方法对地铁车门系统的基础部件进行安全性分析,并将所得结果与传统RPN方法的结果做对比,验证了该方法的可行性和有效性。     

一种平均聚合算子

模糊性是客观世界的固有属性。文章首先阐述了Fuzzy子集及其有关概念,然后讨论Dubois的平均聚合算子M和Yager的有序权平均算子OWA,并证明OWA算子是一种M算子。     

Pythagorean Fuzzy Multiattribute Group Decision Making with Probabilistic Information and OWA Approach

The aim of this paper is to develop a Pythagorean fuzzy multiattribute group decision making (MAGDM) method based on probabilistic information and the ordered weighted averaging (OWA) approach. The Pythagorean fuzzy probabilistic ordered weighted averaging (PFPOWA) operator is presented. It is a new aggregation operator that considers the probabilities and the OWA in the same formulation. Therefore, it is able to take into account the degree of importance that each concept has in the particular problem considered. Some main properties and different particular cases of the PFPOWA operators are studied. Moreover, a method based on the proposed operator for multiattribute group decision making is put forward. Finally, an example showing analysis of a supplier selection is given to verify the effectiveness and practicability of the proposed method.     

A generalized OWA operator

The ordered weighting averaging (OWA) Operator of Yager was introduced to provide a method for nonlinearly aggregating a set of input arguments a_i. A fundamental aspect of the OWA operator is a reordering step in which the input arguments are rearranged according to their values. Recently, a generalized OWA operator was described in which each input argument a_i has its own priority b_i. The input arguments are then rearranged according to the priorities b_i and not their values. Mitchell and Estrakh extended the generalized OWA by assuming the priorities are not known exactly. In this article we describe an alternative approach to include inexact priorities in the generalized OWA operator. Finally, we give a practical application of the new OWA operator to the problem of noise smoothing in an input picture. ©1999 John Wiley & Sons, Inc.     

Quantitative orness for lattice OWA operators

This paper deals with OWA (ordered weighted average) operators defined on any complete lattice endowed with a t-norm and a t-conorm and satisfying a certain finiteness local condition. A parametrization of these operators is suggested by introducing a quantitative orness measure for each OWA operator, based on its proximity to the OR operator. The meaning of this measure is analyzed for some concrete OWA operators used in color image reduction, as well as for some OWA operators used in a medical decision making process.     

The uncertain OWA operator

The ordered weighted averaging (OWA) operator was introduced by Yager 1 to provide a method for aggregating several inputs that lie between the max and min operators. In this article, we investigate the uncertain OWA operator in which the associated weighting parameters cannot be specified, but value ranges can be obtained and each input argument is given in the form of an interval of numerical values. The problem of ranking a set of interval numbers and obtaining the weights associated with the uncertain OWA operator is studied. © 2002 Wiley Periodicals, Inc.     

An overview of methods for determining OWA weights

The ordered weighted aggregation (OWA) operator has received more and more attention since its appearance. One key point in the OWA operator is to determine its associated weights. In this article, I first briefly review existing main methods for determining the weights associated with the OWA operator, and then, motivated by the idea of normal distribution, I develop a novel practical method for obtaining the OWA weights, which is distinctly different from the existing ones. The method can relieve the influence of unfair arguments on the decision results by weighting these arguments with small values. Some of its desirable properties have also been investigated. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 843–865, 2005.     

Weighted‐selective aggregated majority‐OWA operator and its application in linguistic group decision making

This paper focuses on the aggregation operations in the group decision‐making model based on the concept of majority opinion. The weighted‐selective aggregated majority‐OWA (WSAM‐OWA) operator is proposed as an extension of the SAM‐OWA operator, where the reliability of information sources is considered in the formulation. The WSAM‐OWA operator is generalized to the quantified WSAM‐OWA operator by including the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM‐IOWA operator, with an ordering step, is introduced to the individual fusion strategy. The proposed aggregation operators are then implemented for the case of alternative scheme of heterogeneous group decision analysis. The heterogeneous group includes the consensus of experts with respect to each specific criterion. The exhaustive multicriteria group decision‐making model under the linguistic domain, which consists of two‐stage aggregation processes, is developed in order to fuse the experts’ judgments and to aggregate the criteria. The model provides greater flexibility when analyzing the decision alternatives with a tolerance that considers the majority of experts and the attitudinal character of experts. A selection of investment problem is given to demonstrate the applicability of the developed model.     

Induced ordered weighted averaging operators

We briefly describe the Ordered Weighted Averaging (OWA) operator and discuss a methodology for learning the associated weighting vector from observational data. We then introduce a more general type of OWA operator called the Induced Ordered Weighted Averaging (IOWA) Operator. These operators take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are then aggregated. A number of different aggregation situations have been shown to be representable in this framework. We then show how this tool can be used to represent different types of aggregation models.     

INTUITIONISTIC UNCERTAIN LINGUISTIC WEIGHTED BONFERRONI OWA OPERATOR AND ITS APPLICATION TO MULTIPLE ATTRIBUTE DECISION MAKING

With respect to multiple attribute decision making (MADM) problems, in which attribute values take the form of intuitionistic uncertain linguistic information, a new decision-making method based on the intuitionistic uncertain linguistic weighted Bonferroni OWA operator is developed. First, the score function, accuracy function, and comparative method of the intuitionistic uncertain linguistic numbers are introduced. Then, an intuitionistic uncertain linguistic Bonferroni OWA (IULBOWA) operator and an intuitionistic uncertain linguistic weighted Bonferroni OWA (IULWBOWA) operator are developed. Furthermore, some properties of the IULBOWA and IULWBOWA operators, such as commutativity, idempotency, monotonicity, and boundedness, are discussed. At the same time, some special cases of these operators are analyzed. Based on the IULWBOWA operator, the multiple attribute decision-making method with intuitionistic uncertain linguistic information is proposed. Finally, an illustrative example is given to illustrate the decision-making steps and to demonstrate its practicality and effectiveness.