基于特征加权的支持向量回归机研究

基于统计学习理论的支持向量回归机有比较好的泛化能力,然而当样本含有与该问题不完全相关甚至完全无关的特征时,会使得各个特征对问题的相关程度差异很大,从而使得支持向量回归机的效果受到影响。为了解决这个问题,提出了一种基于特征加权的支持向量回归机。模拟的计算结果显示出此方法的有效性。     

Classification in a normalized feature space using support vector machines

This paper discusses classification using support vector machines in a normalized feature space. We consider both normalization in input space and in feature space. Exploiting the fact that in this setting all points lie on the surface of a unit hypersphere we replace the optimal separating hyperplane by one that is symmetric in its angles, leading to an improved estimator. Evaluation of these considerations is done in numerical experiments on two real-world datasets. The stability to noise of this offset correction is subsequently investigated as well as its optimality.     

一种支持向量机的快速分类算法

鉴于传统支持向量机分类过程的计算量和支持向量的个数成正比,为了提高分类决策的速度,提出一种约简支持向量的快速分类算法,该算法对原始的支持向量进行特定比例的模糊均值聚类操作,按照分类误差最小的原则构建最小线性二乘回归模型,求解新的支持向量系数和决策函数的偏置.人造数据集和标准数据集上的实验表明,约简50%支持向量后,可以在保持分类精度在无统计意义的明显损失的前提下,使得分类速度提高50%.     

Adaptive feature selection via a new version of support vector machine

This paper focuses on feature selection in classification. A new version of support vector machine (SVM) named p-norm support vector machine ( $p\in[0,1]$ ) is proposed. Different from the standard SVM, the p-norm $(p\in[0,1])$ of the normal vector of the decision plane is used which leads to more sparse solution. Our new model can not only select less features but also improve the classification accuracy by adjusting the parameter p. The numerical experiments results show that our p-norm SVM is more effective than some usual methods in feature selection.     

基于随机森林和支持向量机的快速行人检测算法

提出了一种两步走的策略提高静态图像中的行人检测速率和性能。目前,利用梯度直方图（ HOG）+支持向量机（ SVM）依然是一种精度较高的方法,但一方面它难以训练足够多的样本,另一方面它的检测消耗太大。因此先采取随机森林的分类算法,快速地消除图像中的背景,并得到一定的感兴趣区域,再通过SVM进行检测。通过在INRIA库上的实验证明,该算法能够实现预期的双重效果。     

基于语义空间的支持向量机的文本过滤

传统的基于支持向量机的文本过滤,用向量空间模型来表示文本和用户模板,向量空间模型假设特征项之间是线性无关的,该假设引入了许多因具体用词变化不定而带来的词汇噪音信息,影响了基于支持向量机的文本过滤的过滤性能。提出基于语义空间的支持向量机的文本过滤,用语义来表示文本和用户模板。该方法主要通过奇异值分解提取文本的潜在语义空间,在语义空间上训练支持向量机得到用户模板和过滤阈值,文本流上的文本映射到语义空间上,在语义空间上计算用户模板和新文本的相似度。实验表明:该方法的过滤性能可以达到 98. 67%。     

基于Contourlet变换和支持向量机的纹理识别方法

针对变换域中图像纹理识别时如何选择最佳特征向量的问题,利用Contourlet变换的多方向、多尺度选择性和各向异性,将图像从空间域变换到频率域,全面地提取了Contourlet变换分解后低频子带、中频子带和高频子带的特征,输入支持向量机(SVM)分类器进行分类识别。利用Brodatz纹理库进行仿真实验,实验结果表明低频均值方差和高频能量作为组合特征时识别准确率可达98.75%,且特征向量维数少,是在Contourlet变换下表示图像纹理的最优特征。     

基于特征选择和支持向量机的异常检测方法

针对异常检测系统虚警率高、检测率低以及冗余特征对检测系统造成负担的问题,提出一种基于特征选择和支持向量机相结合的异常检测方法.该方法通过构造一种基于分类模型分类准确率计算的特征选择算法,筛选出能够获得分类准确率最高的特征组合,并与支持向量机分类算法相结合,实现数据的异常检测.仿真测试结果表明,该方法具有较高的检测准确率和较低的检测时间,并通过去除噪声特征,降低了系统的数据处理难度.     

基于D-score与支持向量机的混合特征选择方法

F-score作为特征评价准则时,没有考虑不同特征的不同测量量纲对特征重要性的影响。为此,提出一种新的特征评价准则D-score,该准则不仅可以衡量样本特征在两类或多类之间的辨别能力,而且不受特征测量量纲对特征重要性的影响。以D-score为特征重要性评价准则,结合前向顺序搜索、前向顺序浮动搜索以及后向浮动搜索三种特征搜索策略,以支持向量机分类正确率评价特征子集的分类性能得到三种混合的特征选择方法。这些特征选择方法结合了Filter方法和Wrapper方法的各自优势实现特征选择。对UCI机器学习数据库中9个标准数据集的实验测试,以及与基于改进F-score与支持向量机的混合特征选择方法的实验比较,表明D-score特征评价准则是一种有效的样本特征重要性,也即特征辨别能力衡量准则。基于该准则与支持向量机的混合特征选择方法实现了有效的特征选择,在保持数据集辨识能力不变情况下实现了维数压缩。     

基于灰色特征加权支持向量机的二维函数拟合

函数拟合通常是在有限的训练样本下对函数变量之间的关系做出预测,由于在实践中训练样本本身存在噪音和孤立点,用传统的方法进行函数拟合的效果不佳。考虑到不同特征对于回归问题相关程度的不同,研究了以灰色关联度作为权重的特征加权支持向量回归机算法,并推广运用于二维函数的回归拟合。仿真结果表明,灰色特征加权方法较传统支持向量回归机,具有更好的回归拟合能力。     

基于最小二乘支持向量机的多属性决策

分析多属性决策问题, 提出了基于最小二乘支持向量机的多属性决策方法。该方法从决策问题本身出发,构造学习样本,再通过最小二乘支持向量机拟合出多属性效用函数,从而实现对方案的排序。该模型与支持向量机相比,参数少,算法简单、可靠。最后通过算例表明方法的可行性与有效性。     

基于模糊支持向量机与决策树的文本分类器

针对模糊支持向量机在文本分类应用中的隶属度函数确定问题,提出了一种基于模糊支持向量机与决策树的文本分类器的构建方法。该方法不仅考虑了样本与类中心之间的关系,还根据传统支持向量机中包含支持向量且平行于分类面的平面构建切球,来确定类中各个样本之间的关系,由样本点与球的位置关系计算其隶属度,可以合理地区分有效样本和噪音、孤立点样本。并与决策树方法相结合,实现多类分类。实验结果表明,该方法具有良好的分类效果。     

在原始空间用Rosenbrock算法训练线性支持向量机

为了加快并行下降方法（ＣＤ）用于线性支持向量机（ＳＶＭ）时的最终收敛速度,将Ｒｏｓｅｎｂｒｏｃｋ算法（Ｒ）用于线性ＳＶＭ．在内循环,Ｒ 通过解一个单变量子问题来更新狑的一个分量,并同时固定其他分量不变;在外循环,采用Ｇｒａｍ-ｓｃｈｍｉｄｔ过程构建新的搜索方向．实验结果表明,与ＣＤ 相比,Ｒ 加快了最终的收敛,在分类中能更快地获得更高的测试精度．     

A decision based one-against-one method for multi-class support vector machine

The support vector machine (SVM) has a high generalisation ability to solve binary classification problems, but its extension to multi-class problems is still an ongoing research issue. Among the existing multi-class SVM methods, the one-against-one method is one of the most suitable methods for practical use. This paper presents a new multi-class SVM method that can reduce the number of hyperplanes of the one-against-one method and thus it returns fewer support vectors. The proposed algorithm works as follows. While producing the boundary of a class, no more hyperplanes are constructed if the discriminating hyperplanes of neighbouring classes happen to separate the rest of the classes. We present a large number of experiments that show that the training time of the proposed method is the least among the existing multi-class SVM methods. The experimental results also show that the testing time of the proposed method is less than that of the one-against-one method because of the reduction of hyperplanes and support vectors. The proposed method can resolve unclassifiable regions and alleviate the over-fitting problem in a much better way than the one-against-one method by reducing the number of hyperplanes. We also present a direct acyclic graph SVM (DAGSVM) based testing methodology that improves the testing time of the DAGSVM method.     

基于特征向量的最小二乘支持向量机PM2.5浓度预测模型

针对大气中细颗粒物(PM2.5)浓度预测的问题,提出一种预测模型。首先,通过引入综合气象指数综合考虑风力、湿度、温度等因素;然后,结合实际二氧化硫（SO2）浓度、二氧化氮（NO2）浓度、一氧化碳（CO）浓度和PM10浓度等,构成特征向量;最后,利用特征向量和PM2.5浓度数据来建立最小二乘支持向量机（LS-SVM）预测模型。经2013年城市A和城市B环境监测中心的数据预测分析表明,引入综合气象指数后预测的准确性提高,误差降低近30%。说明该模型能够较为准确地预测PM2.5浓度,并具有较高的泛化能力。此外还分析了PM2.5浓度与住院率、医院门诊量的关系,发现了它们的高度相关性。     

Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization

Classification approaches usually present the poor generalization performance with an apparent class imbalance problem. Surely, a measures of the quality of the possible models reflected the remaining uncertainty in the class imbalance on learning. The purpose of our learning method is to lead an attractive pragmatic expansion scheme of the Bayesian approach to assess how well it is aligned with the class imbalance problem. Thus, we propose a method with a model assessment of the interplay between various classification decisions using probability, corresponding decision costs, and quadratic program of optimal margin classifier called: Bayesian Support Vector Machines (BSVMs) learning strategy. In the framework, we did modify in the objects and conditions of primal problem to reproduce an appropriate learning rule for an observation sample. The experiments on several existing data sets showed that BSVMs may appropriately capture the true relationship between the inputs and outputs by experimental evidence.     

Building sparse twin support vector machine classifiers in primal space

Twin support vector machines (TSVM) obtain faster training speeds than classical support vector machines (SVM). However, TSVM augmented vectors lose sparsity. In this paper, a rapid sparse twin support vector machine (STSVM) classifier in primal space is proposed to improve the sparsity and robustness of TSVM. Based on a simple back-fitting strategy, the STSVM iteratively builds each nonparallel hyperplanes by adding one support vector (SV) from the corresponding class at one time. This process is terminated using an adaptive and stable stopping criterion. STSVM learning is implemented by linear equation computing systems through introducing a quadratic function to approximate the empirical risk. The computational results on several synthetic and benchmark datasets indicate that the STSVM obtains a sparse separating hyperplane at a low cost without sacrificing its generalization performance.     

改进的多类支持向量机递归特征消除在癌症多分类中的应用

为处理癌症多分类问题,已经提出了多类支持向量机递归特征消除(MSVM-RFE)方法,但该方法考虑的是所有子分类器的权重融合,忽略了各子分类器自身挑选特征的能力。为提高多分类问题的识别率,提出了一种改进的多类支持向量机递归特征消除(MMSVM-RFE)方法。所提方法利用一对多策略把多类问题化解为多个两类问题,每个两类问题均采用支持向量机递归特征消除来逐渐剔除掉冗余特征,得到一个特征子集;然后将得到的多个特征子集合并得到最终的特征子集;最后用SVM分类器对获得的特征子集进行建模。在3个基因数据集上的实验结果表明,改进的算法整体识别率提高了大约2%,单个类别的精度有大幅度提升甚至100%。与随机森林、k近邻分类器以及主成分分析(PCA)降维方法的比较均验证了所提算法的优势。     

核空间结合样本中心角度的大规模支持向量机

为了提高支持向量机在大规模数据集处理时的精度,提出了基于核空间和样本中心角度的支持向量机算法.在核特征空间下,求得原训练集的两类中心点和两个中心点的超法平面,并获取原训练集样本到超法平面距离和到两中心点中点的比值,用比值最小的n个样本点替代训练集.给出的数学模型显示,该算法不需要计算核空间,比现有的同类缩减策略保留了更多的支持向量数目.结合实例对算法进行了仿真实验,实验结果表明,与同类算法相比,该算法在基本没有降低训练速度的情况下获得了更准确的训练精度.     

Feature selection and classification using support vector machine and decision tree

Breast cancer is one of the human threats which cause morbidity and mortality worldwide. The death rate can be reduced by advanced diagnosis. The objective of this article is to select the reduced number of features the help in diagnosing breast cancer in Wisconsin Diagnostic Breast Cancer (WDBC). This proposed model depicts women who all have no cancer cells or in benign stage later develop into malignant (metastases). Due to the dynamic nature of the big data framework, the proposed method ensures high confidence and low execution time. Moreover, healthcare information growth chases an exponential pattern, and current database systems cannot adequately manage the massive amount of data. So, it is requisite to adopt the “big data” solution for healthcare information.