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.     

基于核方法的数据描述及其在企业关系评价中的应用

数据描述又称为一类分类方法,用于描述现有数据的分布特征,以研究待测试数据是否与该分布相吻合.首先简要叙述了基于核方法的数据描述原理,指出:选择适当的核函数以及与之对应的参数,数据描述可应用于模式聚类中,并且这种聚类方法具有边界紧致、易剔除噪声的优势.针对基于数据描述的聚类方法在确定类别数目和具体样本类别归属上所存在的问题,提出了基于搜索的解决方法,理论分析和实例计算都验证了该方法的可行性.最后将该聚类算法应用到企业关系评价中,取得了较为合理的结果.     

分类属性数据的泛化中心聚类算法

针对采用经典划分思想的聚类算法以一个点来代表类的局限,提出一种基于泛化中心的分类属性数据聚类算法。该算法通过定义包含多个点的泛化中心来代表类,能够体现出类的数据分布特征,并进一步提出泛化中心距离及类间距离度量的新方法,给出泛化中心的确定方法及基于泛化中心进行对象到类分配的聚类策略,一般只需一次划分迭代就能得到最终聚类结果。将泛化中心算法应用到四个基准数据集,并与著名的划分聚类算法K-modes及其两种改进算法进行比较,结果表明泛化中心算法聚类正确率更高,迭代次数更少,是有效可行的。     

土壤分类的模糊集成算法与应用

土壤是一个多性状的连续体,其分类的首选方法是模糊聚类分析.但是模糊聚类分析中现有的基于模糊等价关系的动态聚类法和模糊c-均值法各有利弊,采用其中一种方法聚类肯定存在不足.为此集成两种聚类方法的优点,避其缺点,提出了用基于模糊等价关系的动态聚类方法和方差分析方法确定聚类数目和初始聚类中心,再用模糊c-均值法决定最终分类结果的集成算法,并将其应用到松花江流域土壤分类中,得到了较为切合实际的分类结果.     

聚类分析分类结果合理性考核方法

对聚类分析应用于肿瘤分型时的分类结果合理性考核方法进行探讨.该方法借助于预测强度指标,并结合中位数及95%参考值范围的概念来比较不同聚类方法得到的分类的合理性,在一定程度上弥补了聚类分析方法的应用不足.在对白血病患者分型时取得了很好的效果.     

多类分类的支持向量机的有限步终止Newton算法

多类分类问题是数据挖掘和机器学习领域中一个重要且正在进行研究的课题.最近对该问题提出了-种具有新型结构的K-SVCR方法.与其他方法相比较,此方法最大的优点在于在训练的过程中,能够利用训练数据的所有信息.然而,它又和"一对一"方法一样,对某-个K类分类问题,需要求解K(K-1)/2个二次规划问题,才能把一个模式指派到-个适当的类别中.因此建立一个快速有效的训练算法是非常重要的.在本文中,我们首先在K-SVCR方法的基础上提出了新的模型,然后把新模型转化成-个互补问题,并利用Lagrangian隐函数进-步转化成-个强凸的无约束优化问题.并且为它建立了一个快速地Newton箅法.该算法具有全局收敛和有限步终止的性质.同时通过Sherman-Morrison-Woodbury等式,将算法中需要处理的＄l\timesl＄矩阵(其中是模式的总量)转变成＄(n+1)\times(n+1)＄的矩阵(其中n是模式的维数).对于很多多类分类问题,n远远小于1,这也说明可以有效地实现该算法.初步的实验结果表明该算法在分类的准确度和训练速度方面都有很好的表现.     

一种新城市气温模式分类的聚类算法

城市气温是对城市气候特性评价的一个重要指标.提出核概率聚类算法并将其应用于城市气温的模式分类中,以此寻找城市发展上的共同点.该算法在概率聚类算法上引入了核学习方法的思想,能够很好地处理噪音和孤立点,实现更为准确的聚类.实验结果表明,与相关聚类算法相比,核概率聚类算法聚类效果好,且算法能够很快地收敛.     

一种基于支持向量域描述的区间数分类

研究了基于数据的区间数智能决策分析,提出了一种基于区间数的支持向量域多分类软计算方法,该方法可以直接处理特征空间为区间数的多分类问题,拓展了支持向量域多分类算法应用的范围.     

不平衡数据分类的类依赖属性加权朴素贝叶斯算法改进

朴素贝叶斯分类器(Naive Bayes,NB)是一种简单而有效的分类器,特别适用于中小规模数据分类.但作为以整体分类正确率为指导的传统分类方法,它在不平衡数据分类中对少数类的分类能力较弱.针对此问题,本文采用属性加权的方法增强朴素贝叶斯对于少数类的分类能力.类依赖属性加权朴素贝叶斯(class-specific at...     

弹性判别分析在纸币鉴别中的分类应用

分类在许多领域都是重要问题,弹性判别分析是有效解决多类问题的分类方法.基于纸币的四个属性,应用这个方法来鉴别纸币的真伪.运用统计软件R可以得到:训练集的最高准确率达到99.64%,对应检测集的错误率为0.73%.同时,由于回归模型的多样性,弹性判别分析有多种形式.根据数据的特征可以提出适当的方法,对于纸币鉴别的数据集,最好的弹性判别分析的形式应用了带有适应选择项和样条光滑参数的加性模型.     

基于支持向量机的磨粒识别

由于神经网络的局限性,上个世纪末,支持向量机被提出和发展,它在模式识别方面有广泛的应用发展前途,并由最初的二元分类发展到现在的多元分类.本文根据支持向量机的最新发展,把最小二乘支持向量机应用在磨粒识别上,并取得了好的结果.     

基于DEA和神经网络集成模型的我国基础设施投资有效性预测研究

建立了DEA和神经网络集成的基础设施投资有效性预测模型。该模型首先应用DEA方法,对我国1993-2007年逐期的基础设施投资效率进行评价,得到了用于基础设施投资有效性预测的基本数据。根据对评价结果的投资有效和无效划分建立预测样本,选择多层感知器神经网络,分别对基础设施的规模有效性和技术有效性进行了预测。结果表明基础设施的投资有效性预测具有可行性,而且通过与RBF神经网络、logistic回归和C-支持向量分类机等方法对比,MLP-NN方法的回应率和反查都具有优势,表明应用DEA-MLP-NN进行有效性预测更为有效。     

A decomposition approximation method for multiclass BCMP queueing networks with multiple-server stations

Closed multiclass separable queueing networks can in principle be analyzed using exact computational algorithms. This, however, may not be feasible in the case of large networks. As a result, much work has been devoted to developing approximation techniques, most of which is based on heuristic extensions of the mean value analysis (MVA) algorithm. In this paper, we propose an alternative approximation method to analyze large separable networks. This method is based on an approximation method for non-separable networks recently proposed by Baynat and Dallery. We show how this method can be efficiently used to analyze large separable networks. It is especially of interest when dealing with networks having multiple-server stations. Numerical results show that this method has good accuracy.     

Stability of Fluid Networks with Proportional Routing

In this paper we investigate the stability of a class of two-station multiclass fluid networks with proportional routing. We obtain explicit necessary and sufficient conditions for the global stability of such networks. By virtue of a stability theorem of Dai [14], these results also give sufficient conditions for the stability of a class of related multiclass queueing networks. Our study extends the results of Dai and VandeVate [19], who provided a similar analysis for fluid models without proportional routing, which arise from queueing networks with deterministic routing. The models we investigate include fluid models which arise from a large class of two-station queueing networks with probabilistic routing. The stability conditions derived turn out to have an appealing intuitive interpretation in terms of virtual stations and push-starts which were introduced in earlier work on multiclass networks.     

Multicategory Classification by Support Vector Machines

We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how two-class discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik's Support Vector Machine (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise-linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise-nonlinear classification function. Each piece of this function can take the form of a polynomial, a radial basis function, or even a neural network. For the k > 2-class problems, the SVM method as originally proposed required the construction of a two-class SVM to separate each class from the remaining classes. Similarily, k two-class linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach.     

EXISTENCE OF STRONGLY VALID TOLLS FOR MULTICLASS NETWORK EQUILIBRIUM PROBLEMS＊

In this article,we consider the multiclass network equilibrium problems.A so called strongly valid toll can support any multiclass user equilibrium flow pattern as a system minimum when the system obje...     

Boosting the Generalized Margin in Cost-Sensitive Multiclass Classification

The boosting algorithm is one of the most successful binary classification techniques due to its relative immunity to overfitting and flexible implementation. Several attempts have been made to extend the binary boosting algorithm to multiclass classification. In this article, a novel cost-sensitive multiclass boosting algorithm is proposed that naturally extends the popular binary AdaBoost algorithm and admits unequal misclassification costs. The proposed multiclass boosting algorithm achieves superior classification performance by combining weak candidate models that only need to be better than random guessing. More importantly, the proposed algorithm achieves a large margin separation of the training sample while attaining an L₁-norm constraint on the model complexity. Finally, the effectiveness of the proposed algorithm is demonstrated in a number of simulated and real experiments. The supplementary files are available online, including the technical proofs, the implemented R code, and the real datasets.     

Interactive visualization in multiclass learning: integrating the SASSC algorithm with KLIMT

The paper deals with multiclass learning from the perspective of analytically interpreting the results of the analysis as well as that of navigating into them by using interactive visualization tools. It is showed that by combining the Sequential Automatic Search of Subset of Classifiers (SASSC) algorithm with the interactive visualization of classification trees provided by the Klassification—Interactive Methods for Trees (KLIMT) software it is possible to highlight important information deriving from the knowledge extraction process without neglecting the prediction accuracy of the classification method. Empirical evidence from two benchmark datasets demonstrates the advantages deriving from the joint use of SASSC and KLIMT.     

上市公司经营业绩的分类与评价

首先运用R型聚类分析,从反映经营状况的9项指标中,筛选出5项典型指标作为分类指标,然后根据分类指标,运用Q型聚类中的系统聚类分析法将机械工业45家上市公司分成5类,并进一步按盈利性、成长性和财务安全性对每一类公司的业绩状况进行总体归纳评价.在此基础上,运用理想点法并考虑指标的重要性,对每一类各个公司的业绩进行评价和排序,通过分类与评价得出了一些有价值的结论.