基于方向多尺度变换和统计建模的纹理分类方法

纹理是图像分析和识别中经常使用的关键特征, 而小波变换则是图像纹理表示和分类中的常用工具. 然而, 基于小波变换的纹理分类方法常常忽略了小波低频子带信息, 并且无法提取图像纹理的块状奇异信息. 本文提出小波子带系数的局部能量直方图建模方法、轮廓波特征的Poisson 混合模型建模方法和基于轮廓波子带系数聚类的特征提取方法, 并将其应用于图像纹理分类上. 基于局部能量直方图的纹理分类方法解决了小波低频子带的建模难题, 基于Poisson 混合模型的纹理分类方法则首次将Poisson 混合模型用于轮廓子带特征的建模, 而基于轮廓波域聚类的纹理分类方法是一种快速的分类方法. 实验结果显示, 本文所提出的三类方法都超过了当前典型的纹理分类方法.     

Classification Problem for Graphs and Lattices is Wild

The classification problem for finite and infinite algebraic lattices has also been extensively addressed. A wild classification problem contains the classification problem of pairs of matrices up to simultaneous similarity. In this paper, we prove that the classification problem for graphs is wild by reducing the classification problem for finite 2-nilpotent p-groups to the classification problem for graphs.     

基于GBMTS算法的不平衡数据分类研究

解决不平衡数据分类问题,在现实中有着深远的意义。马田系统利用单一的正常类别构建基准空间和测量基准尺度,并由此建立数据分类模型,十分适合不平衡数据分类问题的处理。本文以传统马田系统方法为基础,结合信噪比及F-value、G-mean等分类精度,建立了基于遗传算法的基准空间优化模型,同时运用Bagging集成化算法,构造了改进马田系统模型算法GBMTS。通过对不同分类方法及相关数据集的实验分析,表明:GBMTS算法较其他分类算法,更能够有效的处理不平衡数据的分类问题。     

考虑消费者选择行为的农产品质量分级博弈分析

质量分级是农产品实现优质优价的重要手段,但目前只有少数农产品实现了分级销售。基于消费者的质量优选行为,构建了生产商和零售商实施质量分级的博弈模型,分析了不同主体实施质量分级对产品最优定价及利润的影响。结论表明,农产品质量分级优于不分级;当高质量产品消费者的流失率低于临界值时,零售商质量分级能够实现供应链系统利润最大。据此,建议政府出台相关支持政策,鼓励零售商对农产品进行质量分级。     

Using a similarity measure for credible classification

This paper concerns classification by Boolean functions. We investigate the classification accuracy obtained by standard classification techniques on unseen points (elements of the domain, {0,1}ⁿ, for some n) that are similar, in particular senses, to the points that have been observed as training observations. Explicitly, we use a new measure of how similar a point x∈{0,1}ⁿ is to a set of such points to restrict the domain of points on which we offer a classification. For points sufficiently dissimilar, no classification is given. We report on experimental results which indicate that the classification accuracies obtained on the resulting restricted domains are better than those obtained without restriction. These experiments involve a number of standard data-sets and classification techniques. We also compare the classification accuracies with those obtained by restricting the domain on which classification is given by using the Hamming distance.     

基于非线性变换的PCP-C模型及其在地下水动态分类中的应用

为了克服目前地下水动态分类方法中存在的不能揭示分类指标空间到类型空间的非线性映射关系、方法复杂、计算量大等缺陷,可采用基于非线性变换的主成分投影(PCP)-聚类(C)模型,对地下水动态进行分类.方法首先对分类指标数据进行对数中心化变换,然后应用主成分投影法将变换后的多维指标向量映射到最优一维向量空间,并根据各样本指标在一维向量空间的投影值进行聚类分析,由此得到地下水动态分类结果.地下水动态分类结果表明,建议方法概念清晰,结构简单,计算简便,分类结果可信,是一种有效的地下水动态分类方法.     

ABC库存分类的多指标体系及其信息熵方法

库存合理分类对企业的库存管理非常重要.在现有ABC库存分类指标体系的基础之上,指出需求波动对库存分类的重要性,设计了考虑需求波动系数的.ABC库存分类多指标体系;进一步,分析了指标的差异性对库存分类的影响,提出了基于信息熵的多指标ABC库存分类方法.与已有文献对比分析表明,所设计的多指标分类体系综合考虑了库存物资的不同属性;对某家电企业的案例分析可以看出,所提出的.ABC库存分类的多指标体系及其信息熵方法有助于企业制定合理的库存决策,可有效提高企业的管理绩效.     

Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model

Classification is concerned with the development of rules for the allocation of observations to groups, and is a fundamental problem in machine learning. Much of previous work on classification models investigates two-group discrimination. Multi-category classification is less-often considered due to the tendency of generalizations of two-group models to produce misclassification rates that are higher than desirable. Indeed, producing “good” two-group classification rules is a challenging task for some applications, and producing good multi-category rules is generally more difficult. Additionally, even when the “optimal” classification rule is known, inter-group misclassification rates may be higher than tolerable for a given classification model. We investigate properties of a mixed-integer programming based multi-category classification model that allows for the pre-specification of limits on inter-group misclassification rates. The mechanism by which the limits are satisfied is the use of a reserved judgment region, an artificial category into which observations are placed whose attributes do not sufficiently indicate membership to any particular group. The method is shown to be a consistent estimator of a classification rule with misclassification limits, and performance on simulated and real-world data is demonstrated.     

煤炭资源资产分类方法比较研究

煤炭资源资产评估是实行煤炭资源资产化管理的重要基础工作 ,而煤炭资源资产分类的可靠性是有效完成这一工作的前提条件 .针对煤炭资源资产的特点 ,建立了多个分类模型——基于模糊聚类的分类模型、基于 BP网络的分类模型及基于自适应共振理论网络的分类模型 ,编制了相应的分类程序软件包 ,实例运行效果证明了模型的有效性 .文中还对所建模型进行了比较和分析 ,提出了具体应用的建议 .     

Lie symmetries and form-preserving transformations of reaction-diffusion-convection equations

New Lie symmetry classification of the known class of reaction-diffusion-convection equations is presented. The classification method is based on combining the standard group classification method and the form-preserving transformation approach.     

A classification of one-dimensional affine rank three graphs

The rank three subgroups of a one-dimensional affine group over a finite field were classified in 1978 by Foulser and Kallaher. Although one can use their results for a classification of corresponding rank three graphs, the author did not find such a classification in a literature. The goal of this note is to present such a classification. It turned out that graph classification is much simpler than the group one. More precisely, it is shown that the graphs in the title are either the Paley graphs or one of the graphs constructed by Van Lint and Schrijver or by Peisert. Our approach is based on elementary group theory and does not use the classification of rank three affine groups.     

Minimizing deviations from the group mean: A new linear programming approach for the two-group classification problem

This paper proposes a new linear programming approach to solve the two-group classification problem in discriminant analysis. This new approach is based on an idea from cluster analysis that objects within the same group should be more similar than objects between groups. Consequently, it is desirable for the classification score of an object to be nearer to its mean classification score, but further from the mean classification score of the other group. This objective is accomplished by minimizing the total deviation of the classification scores of the objects from their group mean scores in a linear programming approach. When applied to an actual managerial problem and simulated data, the proposed linear programming approach performs well both in groups separation and group-membership predictions of new objects. Moreover, this new approach has an advantage of obtaining more stable classification function across different samples than most of the existing linear programming approaches.     

On the classification of weighing matrices and self‐orthogonal codes

We provide a classification method of weighing matrices based on a classification of self‐orthogonal codes. Using this method, we classify weighing matrices of orders up to 15 and order 17, by revising some known classification. In addition, we give a revised classification of weighing matrices of weight 5. A revised classification of ternary maximal self‐orthogonal codes of lengths 18 and 19 is also presented. © 2011 Wiley Periodicals, Inc. J Combin Designs 20:40–57, 2012     

Finite vertex-primitive edge-transitive metacirculants

A classification is given of finite metacirculants which are vertex-primitive and edge-transitive. The classification forms a core part of a series of papers towards a classification of edge-transitive metacirculants.     

A pentatonic classification of extreme events

In this paper we present a classification of the extreme events – very small and very large outcomes – of positive-valued random variables. The classification distinguishes five different categories of randomness, ranging from the very ‘mild’ to the very ‘wild’. In analogy with the common five-tone musical scale we term the classification ‘pentatonic’. The classification is based on the analysis of the inherent Gibbsian ‘forces’ and ‘temperatures’ existing on the logarithmic scale of the random variables under consideration, and provides a statistical-physics insight regarding the nature of these random variables. The practical application of the pentatonic classification is remarkably straightforward, it can be performed by non-experts, and it is demonstrated via an array of examples.     

基于多重指标下ABC分类法的煤矿库存物资分类

传统的ABC分类法基于年销售额单一度量标准,容易忽视其它影响库存物资的因素。鉴于此,本文应用多重指标的ABC分类法对郑州煤电物资供销公司的库存物资进行分类。文中比较了两个指标(月平均使用金额与提前期)、三个指标(月平均使用金额、月平均单位库存成本或月平均单位成本与提前期)及五个指标(月平均使用数量、提前期、供应商数量、月平均单位库存成本、月平均销售成本)的分类效果,另外,提前期的取值也分别采用最大值、最小值、平均值进行了计算。与现场管理者的经验值比较,结果表明采用两个指标(月平均使用金额与提前期)且提前期取最小值的吻合效果最好。本研究成果可应用于煤矿物资的分类,可根据具体实际情况,添加度量物资重要性的指标。     

Kalman particle swarm optimized polynomials for data classification

Data classification is an important area of data mining. Several well known techniques such as decision tree, neural network, etc. are available for this task. In this paper we propose a Kalman particle swarm optimized (KPSO) polynomial equation for classification for several well known data sets. Our proposed method is derived from some of the findings of the valuable information like number of terms, number and combination of features in each term, degree of the polynomial equation etc. of our earlier work on data classification using polynomial neural network. The KPSO optimizes these polynomial equations with a faster convergence speed unlike PSO. The polynomial equation that gives the best performance is considered as the model for classification. Our simulation result shows that the proposed approach is able to give competitive classification accuracy compared to PNN in many datasets.     

基于核主元分析的带可变惩罚因子最小二乘模糊支持向量机模型及其在信用分类中的应用

信用分类是信用风险管理中一个重要环节,其主要目的是根据信用申请客户提供的资料从申请客户中区分出可信客户和违约客户,以便为信用决策者提供决策依据.为了正确区分不同的信用客户,特别是违约客户,结合核主元分析和支持向量机算法构造基于核主元分析的带可变惩罚因子最小二乘模糊支持向量机模型对信用数据进行了分类处理.在基于核主元分析的带可变惩罚因子最小二乘模糊支持向量机模型中,首先对样本数据进行预处理,然后利用核主元分析以非线性方式降低数据的维数,最后利用带可变惩罚因子最小二乘模糊支持向量机模型对降维后数据进行分类分析.为了验证,选择两个公开的信用数据集来进行实证分析.实证结果表明：基于核主元分析的带可变惩罚因子最小二乘模糊支持向量机模型取得了较好的分类结果,可为信用决策者提供重要的决策参考依据.