Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Fuzzy multi-criteria decision-making approach with incomplete information based on evidential reasoning

The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.

The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems

For the moment, the representative and hot research is decision-theoretic rough set (DTRS) which provides a new viewpoint to deal with decision-making problems under risk and uncertainty, and has been applied in many fields. Based on rough set theory, Yao proposed the three-way decision theory which is a prolongation of the classical two-way decision approach. This paper investigates the probabilistic DTRS in the framework of intuitionistic fuzzy information system (IFIS). Firstly, based on IFIS, this paper constructs fuzzy approximate spaces and intuitionistic fuzzy (IF) approximate spaces by defining fuzzy equivalence relation and IF equivalence relation, respectively. And the fuzzy probabilistic spaces and IF probabilistic spaces are based on fuzzy approximate spaces and IF approximate spaces, respectively. Thus, the fuzzy probabilistic approximate spaces and the IF probabilistic approximate spaces are constructed, respectively. Then, based on the three-way decision theory, this paper structures DTRS approach model on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. So, the fuzzy decision-theoretic rough set (FDTRS) model and the intuitionistic fuzzy decision-theoretic rough set (IFDTRS) model are constructed on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. Finally, based on the above DTRS model, some illustrative examples about the risk investment of projects are introduced to make decision analysis. Furthermore, the effectiveness of this method is verified.

Lower Approximation Reduction in Ordered Information System with Fuzzy Decision

Attribute reduction is one of the most important problems in rough set theory. This paper introduces the concept of lower approximation reduction in ordered information systems with fuzzy decision. Moreover, the judgment theorem and discernable matrix are obtained, in which case an approach to attribute reduction in ordered information system with fuzzy decision is constructed. As an application of lower approximation reduction, some examples are applied to examine the validity of works obtained in our works..

Attribute Reduction in Interval and Set-Valued Decision Information Systems

In many practical situation, some of the attribute values for an object may be interval and set-valued. This paper introduces the interval and set-valued information systems and decision systems. According to the semantic relation of attribute values, interval and set-valued information systems can be classified into two categories: disjunctive (Type 1) and conjunctive (Type 2) systems. In this paper, we mainly focus on semantic interpretation of Type 1. Then, we define a new fuzzy preference relation and construct a fuzzy rough set model for interval and set-valued information systems. Moreover, based on the new fuzzy preference relation, the concepts of the significance measure of condition attributes and the relative significance measure of condition attributes are given in interval and set-valued decision information systems by the introduction of fuzzy positive region and the dependency degree. And on this basis, a heuristic algorithm for calculating fuzzy positive region reduction in interval and set-valued decision information systems is given. Finally, we give an illustrative example to substantiate the theoretical arguments. The results will help us to gain much more insights into the meaning of fuzzy rough set theory. Furthermore, it has provided a new perspective to study the attribute reduction problem in decision systems.

Fuzzy Privacy Decision for Context-Aware Access Personal Information

A context-aware privacy protection framework was designed for context-aware services and privacy control methods about access personal information in pervasive environment. In the process of user＇s privacy decision, it can produce fuzzy privacy decision as the change of personal information sensitivity and personal information receiver trust. The uncertain privacy decision model was proposed about personal information disclosure based on the change of personal information receiver trust and personal information sensitivity. A fuzzy privacy decision information system was designed according to this model. Personal privacy control policies can be extracted from this information system by using rough set theory. It also solves the problem about learning privacy control policies of personal information disclosure.

信息不完全的Fuzzy群体多准则决策的规划方法

提出了一种准则权系数和决策者权系数信息都不完全,且决策者存在风险态度的Fuzzy群体多准则决策方法。该方法通过利用准则权系数的不完全信息构造各方案针对不同决策者的Fuzzy线性规划,利用Fuzzyα 截集和区间数的性质,将Fuzzy线性规划转化为线性规划,求解综合后的线性规划得到各方案针对不同决策者的评价模糊数。然后利用决策者的不完全信息构造Fuzzy线性规划,将Fuzzy线性规划转换成为一般线性规划问题,求解综合后的线性规划问题,得到方案的群体评价模糊数。对决策者的风险态度进行集成得到群体风险态度,利用模糊数的距离公式将群体风险态度与方案的群体评价模糊进行集成得到整个方案集的一个排序。对准则值是梯形模糊数的信息不完全的Fuzzy群体决策方法的实现过程进行了详细讨论。最后通过实例说明该方法的可行性和有效性。

区域投资环境评价的Fuzzy群体多准则决策方法

区域投资环境评价是一种信息不完全确定的多准则决策问题。针对这类问题,利用传统TOPSIS方法的基本思路,提出了一种信息不完全确定的Fuzzy群体多准则决策方法。在该方法中,首先对每一决策者,利用TOPSIS方法进行方案的准则集成,求解多目标优化模型得到各准则的权系数,并计算方案的准则集成值;然后进行方案的群体集成,通过求解线性规划模型得到决策者的权重,进而得到方案集的整体排序。最后用实例验证了方法的可行性和有效性。

带Fuzzy外壳和Boole心脏的险象识别逻辑

结合新近发展起来的Fuzzy命题逻辑的思想和组合线路险象识别技术的背景 ,改造G del蕴涵算子和公式代数的赋值格 ,构造了另一种带Fuzzy外壳和Boole心脏的险象识别逻辑 ,对其进行了语义地研究 ,得到若干有趣的结果 .

Assessing Watershed Vulnerability in Bernalillo County, New Mexico Using GIS-Based Fuzzy Inference

Watershed vulnerability was assessed for Bernalillo County, New Mexico using a multi-criteria Fuzzy Inference System (FIS) implemented in a Geographic Information System (GIS). A vulnerability map was produced by means of a weighted overlay analysis that combined soil erosion and infiltration maps derived from the FIS methodology. Five vulnerability classes were stipulated in the model: not vulnerable (N), slightly vulnerable (SV), moderately vulnerable (MV), highly vulnerable (HV), and extremely vulnerable (EV). The results indicate that about 88% of the study area is susceptible to slight (SV) to moderate vulnerability (MV), with 11% of the area subject to experience high or extreme vulnerability (HV/EV). For land use and land cover (LULC) classifications, shrub land was identified to experience the most vulnerability. Weighted overlay output compared similarly with the results predicted by Revised Universal Soil Loss Equation (RUSLE) model with the exception of the not vulnerable (N) class. The eastern portion of the county was identified as most vulnerable due to its high slope and high precipitation. Herein, structural stormwater control measures (SCMs) may be viable for managing runoff and sediment transport offsite. This multi-criteria FIS/GIS approach can provide useful information to guide decision makers in selection of suitable structural and non-structural SCMs for the arid Southwest.

Evaluating Watershed Vulnerability in Bernalillo County, New Mexico Using Expert Testimony, Fuzzy Analytic Hierarchy Process, and GIS

Geographic Information System (GIS) software was used to create a watershed vulnerability model for Bernalillo County, New Mexico. Watershed vulnerability was investigated as a function of soil erosion and infiltration criteria: precipitation, land slope, soil erodibility (K-factor), vegetation cover (NDVI), land use, drainage density, saturated hydraulic conductivity, and hydrologic soil group. Respective criteria weights were derived using a Fuzzy Analytic Hierarchy Process (FAHP) supported by expert opinion. A survey of 10 experts, representing New Mexico Institute of Mining and Technology (NMT), the New Mexico Bureau of Geology and Mineral Resources (NMBGMR), and the United States Geologic Survey (USGS), provided model input data for an integrated pair-wise comparison matrix for soil erosion and for infiltration. Individual criteria weights were determined by decomposing the respective fuzzy synthetic extent matrix using the centroid method. GIS layers were then combined based on criteria weights to produce maps of soil erosion potential and infiltration potential. A composite watershed vulnerability map was generated by equal weighting of each input map. Model results were categorized into five vulnerability categories: not vulnerable (N), slightly vulnerable (SV), moderately vulnerable (MV), highly vulnerable (HV), and extremely vulnerable (EV). The resulting FAHP/GIS model was used to generate a watershed vulnerability map of discrete areas in Bernalillo County, which may be vulnerable to stormwater run-off events and soil erosion. Such high volume run-off events can cause erosion damage to property and infrastructure. Alternatively, in areas near urban development, stormwater run-off may contribute non-point-source pollutant contamination of New Mexico’s surface water resources. The most problematic areas in Bernalillo County are present in the Eastern and Northwestern portions. However, less than 1% of the total area lies within the lowest and highest vulnerability categories with the majority centered around moderate vulnerability. The results of the model were compared with a previously published crisp AHP method. Both methods showed similar regional vulnerability trends. This MCDS/GIS approach is intended to provide support to local governments and decision makers in selection of suitable structural or nonstructural stormwater control measures.

Optimization in Site Selection of Wind Turbine for Energy Using Fuzzy Logic System and GIS—A Case Study for Gujarat

The development of new wind energy project requires studying of many parameters to achieve maximum benefits at the cost of minimum environmental impacts. Using Geographic Information System (GIS), an analytical framework has been developed in this paper with fuzzy logic to evaluate the suitable site for turbines for optimum energy output. The criteria for suitable site for energy optimization are environmental, physical and human factors. The present study helps to assess the appropriate sites for the wind turbines in Gujarat. The result obtained from the study conveys the suitability of the development of wind turbines along the western parts of Gujarat. The suggested model could be used for the future site selection of the wind turbine which in turn could be of orientation for energy planners and decision makers.

三角模糊不完备三支群决策及其在糖尿病诊断中的应用

为同时应对不确定信息表示与风险信息融合对群决策带来的挑战,构建一种三角模糊不完备三支群决策方法,并将其应用于糖尿病诊断决策。首先,针对信息不确定性蕴含的模糊性和不完备性,分别引入三角模糊集和不完备信息系统的概念。通过与多粒度三支决策结合,构建了可调多粒度三角模糊概率粗糙集模型。然后,根据离差最大化法计算属性权重与专家权重,结合ELECTRE(elimination et choice translating reality)方法建立了三角模糊多属性群决策方法。最后,通过对糖尿病患者数据的案例分析和评估,验证了所提方法的可行性和有效性。该方法不仅从不确定信息表示、风险信息融合和最优粒度选择的视角丰富了多粒度三支群决策理论,而且推动了糖尿病智能诊断方面的应用。

含干热岩区域综合能源系统日前优化调度

为了研究干热岩增强地热系统的发电性能和潜力,建立含干热岩综合能源系统的优化调度模型。首先,依据干热岩对流换热和有机朗肯循环发电机理,建立干热岩增强地热系统运行模型,为含干热岩区域综合能源系统的运行和规划提供模型基础;其次,针对系统调度中的风电、光伏不确定性波动,基于模糊信息间隙决策理论对不确定性半径和调度成本进行协同优化,从风险规避和机会寻取两方面量化系统不确定性波动变化范围,并评估相应的风险损失和机会收益;最后,通过算例仿真验证所提模型的有效性。

非完全信息下协作式入侵检测系统检测库配置研究

本文研究了有限时间下非完全信息协作式入侵检测系统(IDS)的检测库配置。针对各入侵检测系统面对不同类型攻击时的最优检测库的配置以及检测库分配的矛盾,提出了一种双层检测库配置方法。第1层研究的是各攻击者的策略制定以及对应入侵检测系统的最优检测库配置方案;第2层研究的是如何解决多个入侵检测系统加载同一检测库的矛盾,给出了基于策略共享的集中式分配方法。第1层研究又可分为2步解决:第1步针对攻击者无法获知系统的真实状态、与入侵检测系统之间存在着信息不对称的问题,提出构建一个基于信念的随机博弈框架,通过后向递归算法求解,并证明该解就是博弈的稳态纳什均衡(SNE)策略;第2步通过求解混合Markov决策过程得到各入侵检测系统的最优检测库配置方案。仿真结果表明,本文所提方法能有效地得到协作式入侵检测系统面对不同类型攻击时的最优检测库配置方案。

基于自信息熵的直觉模糊决策系统的属性约简

针对在直觉模糊集中,利用下近似构建的约简只考虑了下近似而忽略了上近似,从而导致一些信息丢失的问题,基于直觉模糊集的上、下近似提出了3种熵度量,并将其应用于直觉模糊决策信息系统的约简之中。在直觉模糊决策信息系统上定义用于描述直觉模糊关系的3种不确定性度量,分别为平均决策指数、平均安全决策指数以及平均风险决策指数,并在此基础上依次提出了条件信息熵、条件粗糙熵和自信息熵,基于自信息熵给出了相应的约简定义以及属性约简算法。在多个数据集上的实验表明,所提出的属性约简算法与其他算法相比,约简结果更具有优越性以及鲁棒性。

不完备异构冲突信息系统中的极大一致联盟区间集族

作为处理不确定性问题的有效工具,三支决策已被广泛应用于冲突分析研究中。然而,现有的三支冲突分析模型大多基于单一类型的冲突信息系统,难以应对实际应用中代理对议题具有多类型评价值和缺失值的情况。同时,已有联盟集的定义通常基于给定代理,即联盟集中的代理与给定代理具有联盟关系,但不一定两两联盟。为了解决这些问题,构建了基于不完备异构冲突信息系统的三支冲突分析模型,并提出了代理两两相关的极大一致联盟区间集族的定义及其构造算法。首先,通过定义代理不同类型评价值的支持度和反对度,将对单个议题单维度多类型评价值的不完备异构冲突信息系统转化为双维度同类型评价值的二维模糊不完备冲突信息系统。其次,通过定义代理之间的乐观与悲观距离函数,构造了基于代理的联盟、冲突、中立区间集。最后,定义了极大一致联盟区间集族,并利用极大团的枚举算法获取极大一致联盟区间集族。

不完备混杂信息系统的属性约简

海量数据的复杂性、不确定性和高维性引发了属性约简的需求。对于完备决策系统,香农熵及其变体已被广泛用于评估属性的重要性。对于不完备混杂信息系统,基于信息增益的属性约简研究较少。在模糊粗糙集框架,针对不完备混杂信息系统提出了一种基于信息增益的属性约简方法,构造了适用于不同数据类型的距离函数来度量不同对象之间的距离,然后计算从每个属性中获得的信息,最后选择符合条件的属性子集。为了验证所提出方法在不完备混杂信息中的有效性,在几个公开数据集上进行了实验。实验结果表明提出的算法相较于基于增益比的属性约简算法、基于Alpha-investing流属性约简算法以及基于邻域条件互信息的交互属性约简算法在处理不完备混杂数据上是有效的。