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 entropy design for non convex fuzzy set and application to mutual information

Fuzzy entropy was designed for non convex fuzzy membership function using well known Hamming distance measure.The proposed fuzzy entropy had the same structure as that of convex fuzzy membership case.Design procedure of fuzzy entropy was proposed by considering fuzzy membership through distance measure,and the obtained results contained more flexibility than the general fuzzy membership function.Furthermore,characteristic analyses for non convex function were also illustrated.Analyses on the mutual information were carried out through the proposed fuzzy entropy and similarity measure,which was also dual structure of fuzzy entropy.By the illustrative example,mutual information was discussed.

Multi-objectives fuzzy optimization model for ship form demonstration based on information entropy

Selecting optimization ship form scheme is an important content in the process of concept design of ship. Multi-objective fuzzy decision-making model for ship form demonstration is set up according to the fuzzy pattern-recognition theory. Weight coefficients of each target of ship form scheme are determined by information entropy and individual subjective partiality. This model is used to select the optimal ship form scheme, the example shows that the model is exact and the resuh is credible. It can provide a reference for choosing the optimization scheme of ship form.

Relativistic Information Entropy on Uncertainty Analysis

in this paper, a new approach to relativistic information entropy is used to assess some relative uncertainties in structural reliability assessment. This approach is composed of the information theory and the relativistic theory, and can be used to measure the relativity of parameter uncertainty and system uncertainty in structural reliability theory based on the same generalized relativistic reference system. Therefore, the structural reliability assessment can be assessed reasonably by the approach.

Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

With the frequent occurrences of emergency events,emergency decision making(EDM)plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times.It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time,since inappropriate decisions may result in enormous economic losses and social disorder.To handle emergency effectively and quickly,this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough(q-ROPR)set.A novel list of q-ROFR aggregation information,detailed description of the fundamental characteristics of the developed aggregation operators and the q-ROFR entropy measure that determine the unknown weight information of decision makers as well as the criteria weights are specified.Further an algorithm is given to tackle the uncertain scenario in emergency to give reliable and reasonable emergency decisions.By using proposed list of q-ROFR aggregation information all emergency alternatives are ranked to get the optimal one.Besides this,the q-ROFR entropy measure method is used to determine criteria and experts’weights objectively in the EDM process.Finally,through an illustrative example of COVID-19 analysis is compared with existing EDM methods.The results verify the effectiveness and practicability of the proposed methodology.

A Form of Information Entropy

In this paper, by axiomatic way, a form of information entropy will be presented on crisp and fuzzy setting. Information entropy is the unavailability of information about a crisp or fuzzy event. It will use measure of information defined without any probability or fuzzy measure: for this reason it is called general information.

Spatial assessment for groundwater quality based on GIS and improved fuzzy comprehensive assessment with entropy weights

The objective of the research is to evaluate spatial groundwater quality based on improved fuzzy comprehensive assessment model with entropy weights(FCAEW)in geographical information system(GIS)environment.This paper explores the method of comprehensive evaluation of groundwater and sets up an evaluation model applying GIS and FCAEW.Groundwater samples were collected and analyzed from 29 wells in Zhenping County,China.Six parameters were chosen including chloride,sulfate,total hardness,nitrate,fluoride and color.Better spatial interpolation methods for evaluated parameters are found out and selected according to the minimum cross-validation errors from the interpolation methods.FCAEW model was carried out with the help of GIS which makes the evaluating process simpler and easier and more automatically,effectively,efficiently and intelligently.The result embodies the feasibility and effectiveness of FCAEW in GIS when compared with other comprehensive evaluation methods.

Introduce a Novel PCA Method for Intuitionistic Fuzzy Sets Based on Cross Entropy

In this paper, a new method for Principal Component Analysis in intuitionistic fuzzy situations has been proposed. This approach is based on cross entropy as an information index. This new method is a useful method for data reduction for situations in which data are not exact. The inexactness in the situations assumed here is due to fuzziness and missing data information, so that we have two functions (membership and non-membership). Thus, method proposed here is suitable for Atanasov’s Intuitionistic Fuzzy Sets (A-IFSs) in which we have an uncertainty due to a mixture of fuzziness and missing data information. For the demonstration of the application of the method, we have used an example and have presented a conclusion.

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.

基于Rough-Fuzzy的网构软件信任度量模型

针对网构软件信任度量模型缺少自适应性和不能很好地刻画信任关系复杂性和不确定性的问题,提出基于Rough-Fuzzy的网构软件信任度量模型.将Rough-Fuzzy思想引入到对网构软件的信任度量上,并将信息熵理论引入到不同信任属性值的权值计算上,以解决信任属性值计算的不确定性问题.实验表明该度量模型对善意节点和恶意节点的信任度有明显的区分效果,能够抑制恶意节点的信任度增长.并且该模型具有良好的适应性,对于一些信任度量参数,系统可以动态地去调整并且增加或减少信任度量属性,能够很好地适应开放网络环境的动态性特点.

Information Entropy Fuzzy Matter-element Model and Its Application to Regional Eco-Environmental Quality Assessment

Based on the fuzzy characters of eco-environmental quality conception and classification standards, the incompatibility of evaluation indexes, the statistical fluctuation of index values, an information entropy fuzzy matter-element model for evaluating regional eco-environmental quality is proposed by way of comprehensively utilizing such theories as information theory, fuzzy sets and matter-element theory, etc. As a case, the model established here is used to evaluate the eco-environmental quality of Lake Chaohu basin. In the case, the eco-environmental quality standards and the evaluated schemes are indicated as matter-elements, together. Through constructing compound fuzzy matter-element, probability compound fuzzy matter-element and self-information compound fuzzy matter- element, the information entropy of each matter-element （including evaluated schemes and classification standards） is calculated in the end. According to these obtained information entropy values, the evaluated schemes can be not only arranged in quality state order but also classified by classification standards .Study result shows that information entropy fuzzy matter-element model is suitable for regional eco-environmental quality assessment.

Fuzzy信息与熵

本文首先引入 Fuzzy 测度的相关概率,然后定义 Fuzzy 测度空间的熵,进而讨论了 Fuzzy 测度空间熵的性质和信息空间的熵。

采用混合策略联合优化的模糊C-均值聚类信息熵点云简化算法

针对传统聚类算法处理点云简化问题时精度低、耗时长且易丢失特征信息等问题,提出了一种基于动态精英自适应混合策略的鹈鹕算法(DEAMPOA)与加权熵法联合优化的模糊C-均值聚类(FCM)信息熵点云简化算法。采用动态自适应种群混合策略,同时融合了精英反向化思路,显著提升了鹈鹕优化算法(POA)的收敛趋势和全局寻优能力,提高了寻找FCM最优聚类中心的成功率;利用DEAMPOA结合加权熵法对FCM进行优化,提高鲁棒性的同时增强了搜索精度,得到较好的聚类结果;在8种UCI标准数据集上与4种算法对比进行聚类性能评估实验,验证了所提方法综合性能优越;将所提方法与信息熵融合,并应用在三维点云KITTI数据集简化中。实验结果表明:与包围框简化法、随机采样简化法和特征选择简化法对比,所提方法全局误差简化前后点集之间平均欧式距离(MED)指标分别降低了2.25%、6.93%、5.74%,点云简化效果最优且运行速度满足要求。

环境评价系统的Fuzzy信息熵分析模型

本文分析了环境质量评价层序树模式的要素，综合了环境信息的特点，提出Ｆｕｚｚｙ信息熵模型，对环境评价方法进行检验分析。该模型适用于带有随机性和模糊性模型的评定。

地质灾害易发性评价因子分级的AIFFC算法优化

针对地质灾害易发性评价因子分级数不确定的问题,引入自适应膨胀因子模糊覆盖分级方法(fuzzy cover approach for clustering based on adaptive inflation factor,AIFFC)对易发性评价因子分级进行优化。以湖南省湘乡市为研究区,提取了坡度、坡向、高程、年平均降雨量、归一化植被指数、道路、断层、岩性和土地利用9类评价因子,运用AIFFC及自然断点法(natural breakpoint classification,NBC)对连续型因子进行分级,并分别代入加权信息量模型和随机森林模型,获取研究区易发性区划图。采用单因子分级结果精度、灾积比分析和易发性分区结果对AIFFC分级法的优越性进行检验,结果表明:各因子采用AIFFC算法分级的AUC值均高于自然断点法;基于AIFFC的随机森林模型及加权信息量模型的高易发区灾积比分别提升了56.3%、74.6%,低易发区灾积比分别降低了48%、58.1%,AUC值分别提升了7.6%、2.7%。采用AIFFC分级方法优化了地质灾害易发性评价因子分级,显著提高了地质灾害易发性评价的合理性。

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。

基于模糊变换的多模型速度结果融合

在不确定分析基础上,为融合多源模型事故再现速度结果,本文提出一种基于模糊变换的多模型结果融合方法。首先对多模型事故再现速度结果离散化,采用K-means聚类获得聚类结果,以各聚类区间数据极值作子区间;根据熵值法得到各模型的信度,用最小信息熵原理将D-S证据理论融合得到的各专家对模型的打分融入到熵值法模型信度中去,对其进行修正;最后以模型信度为权重集与评判矩阵作模糊变换进行综合评判,得最终融合结果。并通过一真实事故案例进行实际应用。

基于模糊综合评价的户式空调系统优选方案评估方法

针对户式空调系统性能常用评价方法存在评价指标不全面、评价过程复杂及评价结果主观性强等不足,提出1种户式空调系统性能模糊综合评价方法。考虑空调行业绿色低碳的发展要求,在原有评价指标的基础上增加室外环境及技术特性相关指标,采用层次分析法建立户式空调系统性能评价指标体系,采用专家调查法和信息熵法分别对指标权重进行赋值和修正,采用分布函数法和等级比重法分别对定量和定性指标进行隶属度计算,最后进行模糊综合评价。以多联机和热湿分控2种典型户式空调系统为例,分别采用层次分析法和本文方法进行评价,验证本文方法的科学合理性。结果表明:2种评价方法对多联机和热湿分控方案的综合评定等级均为Ⅱ级,对于层次分析法,综合评价值分别为53.72,53.43,2种方案的评价结果几乎一致,2种方案没有明显优劣,难以为空调选型提供参考;对于本文方法,2种方案的评价结果差值明显,可为空调选型提供参考,这是因为本文方法的评价指标更全面,且在模糊评价法和信息熵法的作用下完成复杂问题的量化,可弥补评价过程复杂和评价结果主观性强的不足。

基于前景理论的多阶段混合信息决策方法

针对属性值为混合信息的多阶段决策问题,考虑决策者风险偏好态度,提出一种新的基于动态参考点的前景理论决策方法。通过定义直觉模糊熵,将混合信息一致化为信息效用值,并在传统单阶段参考点设置方法的基础上引入目标参考点,综合发展状态和发展趋势计算得到具有决策者风险偏好波动特点的动态参考点;进一步通过熵权法确定属性的权重范围,并构建考虑偏差度的目标优化模型,在引入前景理论的基础上测算各备选方案的动态前景值。将该决策方法应用到商业银行对小微企业贷款融资的选择案例中,对比分析说明方法的合理性和可行性。

基于改进模糊信息融合的煤矿带式输送机健康诊断

针对煤矿带式输送机运用多传感器信息融合处理冲突证据时产生悖论的问题,提出一种基于相似矩阵和信息熵的改进模糊信息融合算法,引入模糊集理论中的隶属度函数来获取各传感器提供的基本概率赋值,由证据之间的冲突相量,推算出表征证据之间相互支持度的相似度矩阵,以确定各证据的信任度系数,将信任度系数加权平均,然后运用信息熵函数,对冲突证据进行修正,建立了一种新的冲突证据的处理方法,最后运用证据理论合成规则实现模糊信息融合。通过算法比较表明该方法的有效性,同时该方法可有效识别带式输送机的健康诊断状况,且避免误诊现象。