Decision Making in Reinsurance with Induced OWA Operators and Minkowski Distances

The decision to choose a reinsurance program has many complexities because it is difficult to simultaneously achieve high levels in different optimal criteria including maximum gain, minimum variance, and probability of ruin. This article suggests a new method by which, through membership functions, we can measure the distance of each alternative to an optimal result and aggregate it by using different types of aggregations. In this article, particular attention is given to the induced Minkowski ordered weighted averaging distance operator and the induced Minkowski probabilistic ordered weighted averaging distance operator. The main advantage of these operators is that they include a wide range of special cases. Thus, they can adapt efficiently to the specific needs of the calculation processes. By doing so, the reinsurance system can make better decisions by using different scenarios in the uncertain environment considered.     

基于RST的决策树生成与剪枝方法

基于粗糙集理论构建决策树的过程中,通过计算各条件属性相对某分类的边界,选取边界最小的属性作为当前分支的节点,但此方法在多值分类情况下不能直接应用。为此,本文利用明确区的概念作为选取属性的标准,对各候选条件属性,选取相对于整个结果属性的明确区最大的属性作为当前分支的节点。并且基于明确区的概念,提出了一种新
新的对决策树进行剪枝的方法,通过一个实例说明该剪枝方法是简洁有效的.     

Social Network Decision Making with Linguistic Trustworthiness–Based Induced OWA Operators

Classic aggregation operators in group decision making such as the ordered weighted averaging (OWA), induced ordered weighted averaging (IOWA), C‐IOWA, P‐IOWA, and I‐IOWA have shown to be successful tools to provide flexibility in the aggregation of preferences. However, these operators do not take advantage of information related to the interaction between experts. Experts involved in a group decision‐making problem may have developed opinions about the reliability of other experts' judgments, either because they have previous history of interaction with each other or because they have knowledge that informs them on the reliability of other colleagues in the group in solving decision‐making problems in the past. In this paper, and within the framework of social network decision making, we present three new social network analysis based IOWA operators that take advantage of the linguistic trustworthiness information gathered from the experts' social network to aggregate the social group preferences. Their use is analysed with simple but illustrative examples.     

An Empirical Comparison of Pruning Methods for Decision Tree Induction

This paper compares five methods for pruning decision trees, developed from sets of examples. When used with uncertain rather than deterministic data, decision-tree induction involves three main stages—creating a complete tree able to classify all the training examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper concerns the second stage—pruning. It presents empirical comparisons of the five methods across several domains. The results show that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems. They also show that there is no significant interaction between the creation and pruning methods.     

Hybrid Method of Multicriteria Evaluation of Decision Alternatives

In the paper we develop a hybrid decision support method in case of dependent decision criteria. It includes methods of decision theory, fuzzy sets, mathematical programming and statistics, which are adapted to different stages of the multicriteria evaluation of alternatives depending on the specific problem being solved and on the quality of the input expert information. The use of the hybrid method is illustrated by the solution of practical problems.     

A New Family of Induced OWA Operators

A new family of induced ordered weighted averaging (OWA) operators is proposed by invoking the order‐inducing variables at the aggregation step. The objective is to consider the variations in the magnitudes of the order‐inducing variables. The new family of operators include weighted induced OWA, weighted generalized induced OWA, and weighted induced ordered weighted geometric operators. These are further extended to the intuitionistic fuzzy domain. The usefulness of these operators is shown in a supplier selection problem.     

An Analysis of Some Functions That Generalizes Weighted Means and OWA Operators

In this paper, we analyze several classes of functions proposed in the literature to simultaneously generalize weighted means and ordered weighted averaging (OWA) operators: weighted OWA (WOWA) operators, hybrid weighted averaging (HWA) operators, and ordered weighted averaging‐weighted average (OWAWA) operators. Since, in some cases, the results provided by these operators may be questionable, we introduce functions that also generalize both operators and characterize those satisfying a condition imposed to maintain the relationship among the weights.     

决策树剪枝加强的关联规则分类方法

传统关联规则挖掘在面临分类决策问题时,易出现非频繁规则遗漏、预测精度不高的问题。为得到正确合理且更为完整的规则,提出了一种改进方法 DT-AR(decision tree-association rule algorithm),利用决策树剪枝策略对关联规则集进行补充。该方法利用FP-Growth(frequent pattern growth)算法得到关联规则集,利用C4.5算法构建后剪枝决策树并提取分类规则,在进行置信度迭代筛选后与关联规则集取并集修正,利用置信度作为权重系数采取投票法进行分类。实验结果表明,与传统关联规则挖掘和决策树剪枝方法相比,该方法得到的规则在数据集分类结果上更准确。     

一种面向不平衡数据分类的组合剪枝方法

传统的数据分类算法多是基于平衡的数据集创建,对不平衡数据分类时性能下降,而实践表明组合选择能有效提高算法在不平衡数据集上的分类性能。为此,从组合选择的角度考虑不平衡类学习问题,提出一种新的组合剪枝方法,用于提升组合分类器在不平衡数据上的分类性能。使用Bagging建立分类器库,直接用正类(少数类)实例作为剪枝集,并通过MBM指标和剪枝集,从分类器库中选择一个最优或次优子组合分类器作为目标分类器,用于预测待分类实例。在12个UCI数据集上的实验结果表明,与EasyEnsemble、Bagging和C4.5算法相比,该方法不但能大幅提升组合分类器在正类上的召回率,而且还能提升总体准确率。     

基于OWA算子的不同形式偏好信息的群决策方法

研究具有不同形式偏好信息的群决策问题.在描述效用值、序关系值、模糊判断矩阵和AHP判断矩阵等4种形式偏好信息的基础上,首先给出将不同形式的偏好信息转化为模糊判断矩阵形式的计算公式,然后基于OWA算子给出集结各决策者偏好信息和方案优选的方法,最后用一个算例证明了所提出方法的有效性.     

A Method for Decision Making Based on Generalized Aggregation Operators

A new method for decision making based on generalized aggregation operators is presented. We use a concept that it is known in the literature as the index of maximum and minimum level (IMAM). This index uses distance measures and other techniques that are very useful for decision making. In this paper, it is suggested a generalization by using generalized and quasi‐arithmetic means. As a result, it is obtained the generalized and quasi‐arithmetic weighted IMAM (GWIMAM and quasi‐WIMAM) and the generalized ordered weighted averaging IMAM (GOWAIMAM) and the quasi‐OWAIMAM operator. The main advantage is that it provides a parameterized family of aggregation operators that includes a wide range of special cases such as the generalized IMAM and the OWAIMAM. Thus, the decision maker may take decisions according to his degree of optimism and considering ideals in the decision process. We also develop an application of the new approach in a decision‐making problem regarding product selection.     

Sensitivity Analysis of Multiple Criteria Decision Making Method Based on the OWA Operator

Ordered weighted averaging (OWA) operator's weights and orness measure play important roles in the application of the OWA operator to decision‐making problems because the decision result may be different owing to the change in either of them. The aim of this paper is to investigate the influence that the change of OWA operator's weights or orness measure exerts on the decision result. We first give the range of the OWA operator's weights to keep the ranking order of alternatives or the optimal alternative unchanged. Then we make a sensitivity analysis to the orness measure to explore the dependency of the decision result on the orness measure. The results of analysis may provide a decision basis according to which decision makers are able to make a reasonable decision. Finally, a practical example is provided to illustrate the proposed sensitivity analysis methods.     

An Efficient Algorithm for Generating Generalized Decision Forests

A shortcoming of univariate decision tree learners is that they do not learn intermediate concepts and select only one of the input features in the branching decision at each intermediate tree node. It has been empirically demonstrated that cascading other classification methods, which learn intermediate concepts, with decision tree learners can alleviate such representational bias of decision trees and potentially improve classification performance. However, a more complex model that fits training data better may not necessarily perform better on unseen data, commonly referred to as the overfitting problem. To find the most appropriate degree of such cascade generalization, a decision forest (i.e., a set of decision trees with other classification models cascaded to different degrees) needs to be generated, from which the best decision tree can then be identified. In this paper, the authors propose an efficient algorithm for generating such decision forests. The algorithm uses an extended decision tree data structure and constructs any node that is common to multiple decision trees only once. The authors have empirically evaluated the algorithm using 32 data sets for classification problems from the University of California, Irvine (UCI) machine learning repository and report on results demonstrating the efficiency of the algorithm in this paper.     

一种进行K-Means聚类的有效方法

现有的K-Means聚类算法均直接作用于多维数据集上,因此,当数据集基数和聚类属性个数较大时,这些聚类算法的效率极其低下。为此,文中提出一种基于正规格结构的有效聚类方法(KMCRG)。KMCRG算法以单元格为处理对象来有效完成K-Means聚类工作。特别,该算法使用格加权迭代的策略来有效返回最终的K个类。实验结果表明,KMCRG算法在不损失聚类精度的基础上能够快速返回聚类结果。     

OLAP分析中一种有效的下钻路径裁减技术

0LAP技术为企业数据分析提供了极大的便利。然而,复杂的多维结构导致了复杂的下钻路径组合,从而使得用户的数据分析效率低下。解决OLAP分析中用户的探查路径过于复杂冗长是OLAP面临的主要问题之一,但是当前的研究成果大多由于与特定的分析任务相关而不能完全解决该问题。本文提出了一种与分析任务无关的下钻路径裁剪方法。该方法从多维数据结构本身出发将无效的下钻从分析过程中裁剪掉,从而达到了简化分析过程的目标。本文采用向量夹角法评估下钻操作的有效性,并给出了对应的高效下钻路径裁剪算法。该算法以有序的实事表为输入,只需一次扫描即可完成裁剪过程。实验结果证明了本文方法的可行性、高效性、抗稀疏性和抗偏斜性。     

An Efficient Nelson-Oppen Decision Procedure for Difference Constraints over Rationals

Nelson and Oppen provided a methodology for modularly combining decision procedures for individual theories to construct a decision procedure for a combination of theories. In addition to providing a check for satisfiability, the individual decision procedures need to provide additional functionalities, including equality generation.In this paper, we propose a decision procedure for a conjunction of difference constraints over rationals (where the atomic formulas are of the form xy+c or x<y+c). The procedure extends any negative cycle detection algorithm (like the Bellman-Ford algorithm) to generate (1) equalities between all pair of variables, (2) produce proofs and (3) generates models that can be extended by other theories in a Nelson-Oppen framework. All the operations mentioned above can be performed with only a linear overhead to the cycle detection algorithm, in the average case.     

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.     

面向近重复图像匹配的SIFT特征裁减算法

针对图像提取出的SIFT特征数目通常很大、无法精确控制,导致系统效率不高且不稳定的问题,提出一种SIFT特征裁减算法.通过对SIFT关键点的对比度和主曲率比加权来衡量其匹配能力;在定位关键点和计算关键点方向2个步骤增加对关键点的二次筛选,提取出设定阈值数目内对匹配最有效的关键点.实验结果表明,该算法能有效地控制SIFT特征数量,比已有裁减算法具有更高的匹配准确度;与原始未裁减算法相比,在保证匹配准确度的同时,大大提高了系统的效率和稳定性.     

基于广义直图的图像检索方法

文中提出了一种基于广义直方图的图像内容检索方法。首先,论文把图像和它的平滑图像组合起来,形成一个二元组,称之为“广义图像”,广义图像的直方图称为广义直方图;其次,把图像颜色空间聚类算法推广到广义图像;最后,利用传统直方图方法的检索原理,根据广义直方图对广义图像进行检索,从而实现对原图像的检索。实验结果表明,新方法非常有效,与传统直方图方法相比,较大地提高了输出精度。