A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and nonspatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.     

降雨空间分布的模糊熵聚类分析

为了提高降雨量插值精度,充分挖掘降雨变量信息,利用模糊熵聚类分析算法,对流域内雨量站进行模糊熵聚类研究,通过基于可能性分布和距离判定的聚类有效性函数确定模糊熵系数和聚类数,从而得到模糊聚类结果,改进原有的插值方法。以淮河流域蚌埠站以上区域99个雨量站雨量数据,分别在一般情况下和模糊熵聚类情况下做交叉验证,结果显示,模糊熵聚类分析在反距离平方插值法中对降雨精度有所提升。     

一种基于信息熵的聚类结果评价方法

文章基于信息熵理论,将模糊聚类评价方法和决策树分类算法联系起来,提出从信息熵角度用决策树分类算法来评价聚类算法结果的有效性,从而确定最佳的聚类个数;并将该方法应用到证券行业客户忠诚度分析模型的建立中.实验结果表明,该方法可以明显提高聚类的效果,并且使得聚类结果的可解释性强,具有良好的实用价值.     

SDN中基于条件熵和决策树的DDoS攻击检测方法

软件定义网络（software defined network,SDN）作为一种新型网络架构,其转控分离及集中控制的架构思想为网络带来了显著的灵活性,同时为感知全局网络状态提供了便利。分布式拒绝服务攻击（distributed denial of service,DDoS）是一种典型的网络攻击方式。针对SDN网络中进行DDoS攻击检测的问题,提出了一种基于条件熵和决策树的DDoS攻击检测方法,利用条件熵判断当前网络状态,通过分析SDN中DDoS攻击特点,提取用于流量检测的6项重要特征,使用C4.5决策树算法进行网络流量分类,实现对SDN中的DDoS攻击的检测。实验表明,相比于其它研究方法,文中提出的方法不仅具有较高检测精确率和召回率,而且明显缩短了检测时间。     

空间移动对象历史查询的索引新方法

根据空间移动数据库历史查询的要求．本文构建了一种基于三角形外包的空间移动对象历史查询索引结构（TRL树）．并给出了TRL树的性质和相关更新算法,其中对分裂算法做了重点研究。     

最小熵风险决策

本文分析了传统风险决策方法所存在的缺陷,把信息熵准则引入风险决策,提出了最小熵决策方法,并在几种不同的情况下作了推广。     

Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

Spatial aggregations for spatial based decision 

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of nonspatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by nonspatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.     

一种新的模糊决策树模型及其应用

模糊决策树是决策树在模糊环境下的一种推广,虽然其表示形式更符合人类的思维,但在构造时会增加预处理的工作量和创建树时的开销。基于这种情况,提出了一种混合算法,算法保留了较少属性值的Shannon熵,计算多属性和连续属性值模糊化后的模糊熵。将该算法应用于滑坡数据的挖掘中,得到了更易于理解的决策树和有效的规则,与传统算法的性能比较也证明了该算法的有效性。     

A maximum entropy distribution for wave heights of non-linear sea waves

Based on the maximum entropy principle, a probability density function (PDF) for the zero-crossing wave height (H) of random waves is derived as the simple form fn(H)=αHγe－βHn (n is a selectable positive integer) through solving a variational problem subject to some quite general constraints. This PDF maximizes the information entropy of H, and its parameters α, γ and β are expressed explicitly in terms of the distribution moments m, m=1,2,…,n, so it is well competent for describing the distribution of H of nonlinear sea waves with large uncertainty, and its parameters can be simply determined from available data. Comparisons between the PDF with n=3 and n=4 and the observed distributions of H from wave records measured in the East China Sea and in a wind-wave tunnel show fairly satisfying agreements.     

A maximum entropy distribution for wave heights of non-linear sea waves

Based on the maximum entropy principle,a probability density function (PDF) for the zero-crossing wave height (H) of random waves is derived as the simple form fn(H)=αHγe-βHn (n is a selectable positive integer) through solving a variational problem subject to some quite general constraints. This PDF maximizes the information entropy of H, and its parameters α, γ and β are expressed explicitly in terms of the distribution moments m, m=1,2,…,n, so it is well competent for describing the distribution of H of nonlinear sea waves with large uncertainty, and its parameters can be simply determined from available data. Comparisons between the PDF with n=3 and n=4 and the observed distributions of H from wave records measured in the East China Sea and in a wind-wave tunnel show fairly satisfying agreements.     

基于熵权TOPSIS的湖北省城市人居环境质量时空分异研究

城市人居环境质量评价是改善城市人居环境质量、促进城市健康可持续发展的重要基础.该文以湖北省为研究区,从城市人居生活质量、生产质量和生态质量3方面选取18个指标构建城市人居环境质量评价指标体系,采用熵权TOPSIS法和地理探测器模型对湖北省17个市州2013—2018年的城市人居环境质量时空分异及贡献因素进行研究.结果表明： 1) 研究期内湖北省城市人居环境及其生活、生产和生态子系统质量总体上呈提升趋势,各地市州城市人居环境及其生活、生产、生态子系统质量的差距总体上呈缩小趋势;2) 湖北省城市人居环境质量在空间上呈现出“核心集聚、局部连片”的分布特征,且研究期内鄂西地区人居环境质量改善速度最快,鄂中地区次之,鄂东地区最小;3) 规模以上工业企业数密度、人均社会消费品零售总额、人均固定资产投资、城镇化率、公路里程密度是驱动湖北省城市人居环境质量提升的主要贡献因子,但随着时间演变,城市人居环境质量提升的重心将由少数的核心因子向多因子共建转变.     

一种新的 SVM 决策树

SVM在小训练样本、高维情况下具有很好的泛化性能,但它不适用于多类分类.本文分析基本的SVM和多类SVM分类器,重点讨论了SVM决策树,提出了一种结点分类器类集合划分方案来构造SVM决策树.实验结果表明,以这种方法构造的SVM决策树分类器分类性能较好.     

环境调控空间决策支持系统的设计与实现--福建海岸带环境调控决策支持系统

以福建省海岸带环境调控决策支持系统为例 ,对环境模拟建模、地理信息系统、遥感技术、空间数据仓库与数据挖掘融合而成的环境空间决策支持系统的建设与应用进行了探讨 .首先提出了空间决策支持系统可扩展的体系结构 ,并结合应用需求着重介绍环境空间数据仓库的设计、数据挖掘的过程以及若干环境模拟与评价可扩展模块的实现方法 ,最后给出应用系统的功能框架和系统界面 .     

一种新的基于决策熵的决策表约简方法

分析了在知识约简过程中经典粗糙集理论决策表知识约简方法的不足.以知识粗糙熵为基础,将一致和不一致对象分开,提出决策熵的概念及其属性重要性,在此基础上给出约简的判定定理;然后以条件属性子集的决策熵来度量其对决策分类的重要性,提出一种新的知识约简启发式方法.理论分析和实验结果表明,基于决策熵的属性重要性是一种更有效的启发式信息,该方法时间复杂度较低,有助于搜索最小或次优约简.     

带有Yager熵约束的方差-混合熵投资组合优化模型

通过引入可信性方法和Yager熵约束,建立以方差-混合熵（VHE）为风险度量的投资组合优化模型,通过上海证券交易所数据对均值-方差-混合熵模型（MVHEM）、均值-方差模型（MVM）和均值-混合熵（MHEM）模型进行实证比较分析。结果表明,本文所构建的模型可以较好地均衡方差和混合熵在风险控制方面的作用,Yager熵约束的引入可以进一步增强模型的稳定性,在控制风险水平的条件下实现了相对更高的累计收益。     

Multi factors decision making entropy method andits application in engineering management

In order to meet the strict requirements for information in engineering management, the positive interval (0, 1] in Shannon information entropy is extended to the real number interval [-1, 1]. The information theory and the decision theory are combined effectively, and the deficiencies that the traditional Bayes decision-making methods only consider a single factor are made up. The multi-factors engineering decision-making method are proposed, and some critical problems are solved in the practical engineering management decision-making process.     

一种新的基于决策熵的决策表约简方法

分析了在知识约简过程中经典粗糙集理论决策表知识约简方法的不足。以知识粗糙熵为基础,将一致和不一致对象分开,提出决策熵的概念及其属性重要性,在此基础上给出约简的判定定理;然后以条件属性子集的决策熵来度量其对决策分类的重要性,提出一种新的知识约简启发式方法。理论分析和实验结果表明,基于决策熵的属性重要性是一种更有效的启发式信息,该方法时间复杂度较低,有助于搜索最小或次优约简。     

波群长度的最大熵分布

将最大熵分布的概率密度函数fn(G)=αGγexp(-βGn)推广应用于海浪波群长度,该概率密度函数不仅使波群长度G的信息熵最大,符合最大熵原则,而且其形式简单且参量容易由已知观测数据确定,从而便于理论和实际应用.用实验室风浪槽中不同风速下和不同风区处实测的36组波面位移数据对其进行验证,并与至今仍被广泛应用的Longuet-Higgins推导的群长的概率密度函数作比较,结果表明,最大熵分布的概率密度函数与各组实测数据均符合良好,显著地优于Longuet-Higgins推导的概率密度函数,其中n=0.6时拟合效果最好.     

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。