基于粗糙集与支持向量机的故障智能分类方法

结合粗糙集的属性约简与支持向量机的分类功能,提出一种应用粗糙集与支持向量机的故障分类方法。该方法应用粗糙集理论属性约简作为诊断数据预处理器,可将冗余属性从诊断决策表中删除,而不损失有效信息,然后基于支持向量机进行故障分类建模和预测。谊方法可降低故障诊断数据维数及支持向量机在故障分类过程中的复杂度,但不会降低分类性能。将方法应用于某柴油机故障诊断数据的测试分类,结果表明该方法可快速正确的从数据获得故障类剐。     

基于主动学习支持向量机的文本分类

提出基于主动学习支持向量机的文本分类方法,首先采用向量空间模型（VSM）对文本特征进行提取,使用互信息对文本特征进行降维,然后提出主动学习算法对支持向量机进行训练,使用训练后的分类器对新的文本进行分类,实验结果表明该方法具有良好的分类性能。     

一种混合核函数的支持向量机

为了提高支持向量机分类效果、学习能力和外推能力,分析了两种支持向量机的核函数:K型核函数和logistic核函数,构造出一种新的混合核函数的支持向量机,并且对其性能进行了理论分析。把构造出的混合核函数支持向量机与常用核函数构造的向量机应用到二维数据分类与图片分类中进行实验对比。二维数据点和图片分类的实验结果表明,混合核函数的支持向量机的分类效果、学习能力和外推能力,明显优于常用核函数构造的支持向量机。     

基于改进PSO的SVM文本分类研究

提出基于改进PSO优化支持向量机的文本分类方法,首先采用向量空间模型对文本特征进行提取,使用互信息对文本特征进行降维,然后提出改进PSO算法,该算法可实现对SVM参数的精确、稳定、快速优化选择,对支持向量机进行训练,使用训练后的分类器对新的文本进行分类,实验结果表明该方法具有良好的分类性能。     

利用多属性剖面与双边滤波的高光谱影像分类

随着高光谱影像空间分辨率的提高,"同谱异物"和"同谱异物"问题日益凸显,传统基于纯光谱的地物分类方法已不能满足识别精度要求。结合光谱与空间信息进行高光谱影像分类已成为领域研究热点。鉴于此,该文提出了一种基于扩展多属性剖面(Extended Multi-Attribute Profiles,EMAPs)和指引双边滤波的高光谱影像地物分类方法。首先,基于EMAPs提取高光谱影像4种属性的形态学纹理特征,通过级联纹理与光谱特征获得新特征矢量。其次,对获取的新特征进行降维,并采用指引双边滤波器滤除降维影像的噪声,同时保持影像的边缘信息。最后,采用支持向量机实现分类。高光谱影像分类实验表明,所提方法性能优于多种光谱与空间结合的分类方法。     

一种改进的支持向量机降维方法

提出了一种基于支持向量机的改进的降维方法.在输入和特征空间中,特征子集的选取分别根据原始特征每一维对分类的贡献来获得.最后,通过将输入和特征空间中的特征选取联合起来,得到了一种改进的降维方法.实验表明:使用这种方法,在保持对分类准确率不受明显的影响的同时,能大大地提高训练和预测的速度.     

基于Gabor滤波特征和支持向量机的人脸检测

人脸检测是人脸识别与图像及视频检索的一项重要任务。论文提出了一种基于Gabor滤波特征和支持向量机的正面人脸检测方法。算法首先利用了Gabor滤波器的良好的空间位置与方向的选择特性,采用了四种方向的Gabor滤波器提取人脸样本图像特征并用PCA方法对特征降维,然后用已降维的特征训练支持向量机分类器。最后应用SVM分类检测人脸。实验结果证明该方法行是十分有效的。     

一种基于粗糙集属性约简的支持向量异常入侵检测方法

实现了一种粗糙集属性约简和支持向量机分类相结合的异常入侵检测方法。针对网络连接记录特征属性高维的特点,采用粗糙集属性约简的方法压缩数据空间,然后采用υ-SVM两分类方法处理约简和正规化后的数据。基于DARPA1998数据源的实验表明,与采用全部属性的υ-SVM两分类方法相比,该方法具有与之相当的分类精度,但有效地降低了检测时间,减少了存储空间。     

CBFS-SVM及其在故障诊断中的应用

本文结合了属性相关性分析及支持向量机分类机理,提出了一种新的故障诊断方法.首先使用基于属性相关性分析的方法对过程特征变量进行简约,去除冗余的过程信息,并降低过程数据的维数,获得具有代表性的过程特征信息,同时充分利用支持向量机的良好推广性能,提高了预测分类精度.对污水处理过程故障诊断实验结果表明了该方法的优越性.     

基于LSI和SVM相结合的文本分类研究

传统的向量空间模型使用关键词来表示文本,但没有考虑关键词的一词多义和多词一义问题.为了解决该问题,提出了一种潜在语义索引和支持向量机相结合的文本分类方法,使用潜在语义索引方法获得原始特征向量的潜在语义结构.实验结果表明,该方法同单独使用支持向量机的方法相比,分类准确率有小幅度的下降,但特征向量获得了大幅度的降维.     

Classification by nonlinear integral projections

A new method based on nonlinear integral projections for classification is presented. The contribution rate of each combination of the feature attributes, including each singleton, toward the classification is represented by a fuzzy measure. The nonadditivity of the fuzzy measure reflects the interactions among the feature attributes. The weighted Choquet integral with respect to the fuzzy measure serves as an aggregation tool to project the feature space onto a real axis optimally according to an error criterion, and the classifying attribute is properly numerical analysed on the axis simultaneously making the classification simple. To implement the classification, we need to determine the unknown parameters, the values of fuzzy measure and the weight function. This can be done by running an adaptive genetic algorithm on the given training data. The new classifier is tested by recovering the preset parameters from a set of artificial training data generated from these parameters. It also performs well on several real-world data sets. Beyond discriminating classes, this method can also learn the scaling requirements and the respective importance indexes of the feature attributes as well as the relationships among them. A comprehensive discussion on the semantic and geometric meanings of the parameters is given. Moreover, we show how these parameters' values can be used for short-listing important feature attributes to reduce the complexity (dimensions) of the classification problem. Our new method also compares favorably with other methods on some well-known real-world benchmarks.     

A clustering based feature selection method in spectro-temporal domain for speech recognition

Spectro-temporal representation of speech has become one of the leading signal representation approaches in speech recognition systems in recent years. This representation suffers from high dimensionality of the features space which makes this domain unsuitable for practical speech recognition systems. In this paper, a new clustering based method is proposed for secondary feature selection/extraction in the spectro-temporal domain. In the proposed representation, Gaussian mixture models (GMM) and weighted K-means (WKM) clustering techniques are applied to spectro-temporal domain to reduce the dimensions of the features space. The elements of centroid vectors and covariance matrices of clusters are considered as attributes of the secondary feature vector of each frame. To evaluate the efficiency of the proposed approach, the tests were conducted for new feature vectors on classification of phonemes in main categories of phonemes in TIMIT database. It was shown that by employing the proposed secondary feature vector, a significant improvement was revealed in classification rate of different sets of phonemes comparing with MFCC features. The average achieved improvements in classification rates of voiced plosives comparing to MFCC features is 5.9% using WKM clustering and 6.4% using GMM clustering. The greatest improvement is about 7.4% which is obtained by using WKM clustering in classification of front vowels comparing to MFCC features.     

基于GA-CFS和AdaBoost算法的网络流量分类

鉴于特征属性选择在网络流量分类中占据重要地位,为了确定最优特征子集,利用CFS作为适应度函数的改进遗传算法(GA-CFS),从网络流量的249个属性空间中提取主要属性并最终选定18个特征组合作为最优特征子集。通过AdaBoost算法把一系列的弱分类器提升为强分类器,对网络流量进行了深入的分类研究。实验结果表明,基于GA-CFS和AdaBoost的流量组合分类方法较弱分类器具有较高的分类准确率。     

Toward the measure of organizational virtuality

In this article we propose that organizational virtuality can be considered as a set of external as well as internal attributes. We focus on the attributes of internal virtuality, identify their possible dimensions, and refer to those as degrees of freedom. First we consider, in general terms, external and internal aspects of virtuality. Next we discuss organizational structures and introduce possible dimensions of virtuality that include the typology of the professional competencies. The dimensions of virtuality we introduce allow us to construct a multidimensional space of organizational virtuality. By means of this space we are able to convey the different degrees of organizational virtuality and offer some examples. In the final section of the article we test our constructs on real organizations and show a number of results concerning our measures of organizational virtuality. © 2007 Wiley Periodicals, Inc. Hum Factors Man 17: 575–586, 2007.     

基于RS和SVM的客户协同创新伙伴选择

针对客户协同创新伙伴选择时面临决策属性多并且可供决策分析数据样本少的难题,提出了基于粗糙集和支持向量机的客户协同创新中伙伴选择模型。该方法的核心是应用粗糙集进行属性约简作为数据预处理以删除决策中的冗余属性,然后结合支持向量机在处理小样本以及非线性问题上的优势进行客户分类,在保证不会降低分类性能的前提下达到降低数据维数和分类过程中的复杂度的目的。论文最后将该方法应用于一工程实例,结果初步验证了论文提出模型和方法的有效性和可行性。     

Neural classification of finite elements

The work deals with a comparative performance of finite elements, making use of their formulation as vectors (or patterns) in a multi-dimensional space of proper attributes. Since the attributes control the performance, elements defined by similar patterns and related to the same class should show similar behavior. The pattern classification may be carried out with the help a self-organizing feature map of Kohonen with the patterns corresponding to the input space. These networks learn both the distribution and topology of a set of input space. At the end of the learning process, the neurons become selectively tuned to classes of input patterns, thus specifying “family relationships” among the elements. The work makes use of the four attributes: the element dimensionality, its number of nodes, maximum degree of interpolation polynomials and number of degrees of freedom per node, though a more general characterization is also possible.     

动态特征空间的超球体分类算法

在模式识别中,样本在特征空间被直接用作分类或被核函数映射至更高维空间分类。为了提高分类效果,一般使用更好的分隔面和更有效的特征空间。提出了一种使用神经网络寻找到更有效特征空间的动态超球体算法（Dynamic Hypersphere Algorithm,DHA）。DHA采用了动态的特征变换,通过满足优化超球体的条件获得更有效的特征空间,最终通过欧氏距离得到分类结果。在标准数据集上实验证明了DHA能够通过动态的特征变换寻找到有效特征空间,从而获得更好的分类效果。为了进一步证明特征空间的有效性,将DHA应用到MNIST手写体,通过减少训练样本并且将原样本由784维降至10维获得了90.18%的识别率,在不平衡手写体中也获得了较好的效果。     

局部离群点挖掘算法研究

离群点可分为全局离群点和局部离群点.在很多情况下,局部离群点的挖掘比全局离群点的挖掘更有意义.现有的基于局部离群度的离群点挖掘算法存在检测精度依赖于用户给定的参数、计算复杂度高等局限.文中提出将对象属性分为固有属性和环境属性,用环境属性确定对象邻域、固有属性计算离群度的方法克服上述局限;并以空间数据为例,将空间属性与非空间属性分开,用空间属性确定空间邻域,用非空间属性计算空间离群度,设计了空间离群点挖掘算法.实验结果表明,所提算法具有对用户依赖性少、检测精度高、可伸缩性强和运算效率高的优点.     

基于脑功能网络和共空间模式分析脑电情绪识别

传统脑网络的情绪分类将聚类系数、平均最短路径等拓扑属性作为分类特征。针对这些属性易受网络连接阈值和特征选择的影响,难以完全表征不同情绪状态下的网络空间拓扑结构差异的问题,提出了一种基于脑网络和共空间模式的脑电情绪识别方法(EEG emotion classification based on common spatial patterns of brain networks topology,EEC-CSP-BNT)。该算法基于互信息在各个子频段内计算电极间的功能连接矩阵,同时利用共空间模式(common spatial pattern,CSP)分析学习空间滤波器,构建分类特征,最后通过分类器(如Fisher线性判别、支持向量机、K最近邻)实现基于脑电的情绪分类。基于DEAP和SEED数据集的实验结果表明,相比于脑网络拓扑属性,EEC-CSP-BNT能有效提取脑网络拓扑结构的分类信息,提高脑电情绪识别性能。