基于动态粗糙集约简的选择性支持向量机集成

支持向量机集成是提高支持向量机泛化性能的有效手段,个体支持向量机的泛化能力及其之间的差异性是影响集成性能的关键因素。为了进一步提升支持向量机整体泛化性能,提出利用动态粗糙集的选择性支持向量机集成算法。首先在利用Boosting算法对样本进行扰动基础上,采用遗传算法改进的粗糙集与重采样技术相结合的动态约简算法进行特征扰动,获得稳定、泛化能力较强的属性约简集,继而生成差异性较大的个体学习器;然后利用模糊核聚类根据个体学习器在验证集上的泛化误差来选择最优个体;并用支持向量机算法对最优个体进行非线性集成。通过在UCI数据集进行仿真,结果表明算法能明显提高支持向量机的泛化性能,具有较低的时、空复杂性,是一种高效、稳定的集成方法。     

基于多分类支持向量机的网络入侵检测技术

对多分类支持向量机在网络入侵检测中的应用进行了研究,深入探讨了其中的关键技术问题和解决方法,并用KDD1999CUP中的标准入侵检测数据集对文中设计的支持向量机分类器进行了测试评估,将实验结果和BP神经网络方法进行了比较。实验证明,该方法在保持较低误警率的同时有着很好的检测率,并且在训练时间上优于BP网络方法。     

采用RS与CSA-SVM集成的煤与瓦斯突出预测方法

为提高矿井煤与瓦斯突出的预测性能,提出了粗糙集(RS)与克隆选择算法(CSA)―支持向量机(SVM)集成的预测方法。首先应用粗糙集理论对数据集进行约简提取出关键特征指标和数据样本,然后应用支持向量机构建煤与瓦斯突出预测模型,最后应用克隆选择算法和训练样本集预测错误率最小原则智能选择和优化预测模型的参数向量;煤与瓦斯突出预测实验结果验证了该方法的有效性,性能明显优于传统的神经网络预测方法。     

医学诊断中集成学习技术的研究

计算机辅助医学诊断是机器学习技术的一个重要实践,但是在医学诊断中一个重要影响因素来自于数据集中的冗余特征。为了消除诊断中冗余特征对集成学习方法的精度的影响,文章提出了一种PCA-FS-Bagging算法,利用主成份分析进行特征变换来解决这个问题,算法在三个医学诊断数据集上与其它算法比如单个支持向量机、支持向量机Bagging集成等进行了性能比较,结果显示了PCA-FS-Bagging算法具有较好的性能。     

多模态集成阿尔茨海默病和轻度认知障碍分类

为了更有效而准确地诊断阿尔茨海默病(Alzheimer’s disease,AD)和轻度认知障碍(Mild Cognitive Impairment, MCI),文章提出了一种基于多模态数据(MRI、PET 和非成像数据 CSF)的集成支持向量机来分类 AD 和 MCI。该算法使 用集成学习技术来综合利用不同模态数据之间相互作用产生的分类判别信息,并利用支持向量机进行分类。为了评价该 算法的有效性,采用十折(10-fold)交叉验证策略来验证其性能,并在标准数据集 ADNI 上测试算法性能。实验结果表明, 多模态集成支持向量机分类方法的性能优于多模态多核学习和单模态方法。     

局部关注支持向量机算法

针对训练数据集的不均衡性这一问题,结合采样方法和集成方法,提出一种集成支持向量机分类算法。该算法首先对不均衡的训练集进行非监督聚类;然后依靠其底层的局部关注支持向量机进行数据集局部划分,以精确把控数据集间的局部特征;最后通过顶层支持向量机进行分类预测。在UCI数据集上的评测结果显示,该算法与当前流行的算法（如基于采样的核化少数类过采样技术（K-SMOTE）、基于集成的梯度提升决策树（GTB）和代价敏感集成算法（AdaCost）等）相比,分类效果有明显提升,能在一定程度上解决数据集的不均衡问题。     

一种发动机动力系统的非线性建模方法

为了提高发动机动力系统建模的性能,提出了一种支持向量机与克隆选择算法相集成的发动机动力系统建模新方法;该方法采用支持向量机构建发动机动力系统的多输入单输出非线性回归模型,模型的参数向量由改进的克隆选择算法和训练样本集平均绝对误差最小的准则进行自适应整定;实验结果验证了该方法的有效性,模型的精度与稳健性明显优于常规神经网络方法及改进神经网络方法,对发动机动力系统建模与预测研究具有较好参考价值。     

不平衡支持向量机的惩罚因子选择方法

支持向量机在处理不平衡数据集时常常不能取得良好的效果,而基于不同惩罚因子的不平衡支持向量机能够较好地处理这个问题。阐述了支持向量机在不平衡数据集上失效的原因,讨论了不平衡支持向量机的求解算法,提出了一种根据数据集分布的平均密度直接选取惩罚因子的方法,以减少传统交叉验证方法选取参数所需的时间。实验表明,与其他方法相比,这种平均密度方法能够有效提高不平衡支持向量机在不平衡数据集上的识别效果。     

面向类别比例偏移的半监督支持向量机方法*

当未标记数据与有标记数据类别比例偏移较大时,半监督支持向量机性能不佳.基于此情况,文中提出面向类别比例偏移的半监督支持向量机方法.首先估计未标记数据类中心,然后对多个类别比例下的类中心进行最坏情况集成,从而提升半监督支持向量机的性能保障.实验表明,文中方法有效提升半监督支持向量机在类别比例偏移时的性能保障.     

基于深度神经向量机自回归的协同过滤算法

为发掘卷积神经网络在协同过滤预测中的潜力,针对神经自回归模型方法和支持向量机在深度学习中的优势,提出基于深度神经向量机自回归的协同过滤方法。通过将神经网络最后一层的激发函数替换为线性支持向量回归函数的方式,学习基于最小边缘的对数损失。在多个公开数据集上的实验结果表明,该算法在深度神经自回归对协同过滤问题实现较好预测的基础上,线性向量回归函数的使用能更好地提升预测效果。     

Ensemble of support vector machines for land cover classification

This letter presents the results of two different ensemble approaches to increase the accuracy of land cover classification using support vector machines. Finite ensemble approaches, based on boosting and bagging and infinite ensemble created by embedding the infinite hypothesis in the kernel of support vector machines, are discussed. Results suggest that the infinite ensemble approach provides a significant increase in the classification accuracy in comparison to the radial basis function kernel‐based support vector machines. While using finite ensemble approaches, bagging works well and provides a comparable performance to the infinite ensemble approach, whereas boosting decreases the performance of support vector machines. Comparison in terms of computational cost suggests that finite ensemble approaches require a large processing time in comparison to the infinite ensemble approach.     

Application of support vector machines in scour prediction on grade-control structures

Research into the problem of predicting the maximum depth of scour on grade-control structures like sluice gates, weirs and check dams, etc., has been mainly of an experimental nature and several investigators have proposed a number of empirical relations for a particular situation. These traditional scour prediction equations, although offer some guidance on the likely magnitude of maximum scour depth, yet applicable to a limited range of the situations. It appears from the literature review that a regression mathematical model for predicting maximum depth of scour under all circumstances is not currently available. This paper explores the potential of support vector machines in modeling the scour from the available laboratory and field data obtained form the earlier published studies. To compare the results, a recently proposed empirical relation and a feed forward back propagation neural network model are also used in the present study. The outcome from the support vector machines-based modeling approach suggests a better performance in comparison to both the empirical relation and back propagation neural network approach with the laboratory data. The results also suggest an encouraging performance by the support vector machines learning technique in comparison to both empirical relation as well as neural network approach in scaling up the results from laboratory to field conditions for the purpose of scour prediction.     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Least squares support vector machines ensemble models for credit scoring

Due to recent financial crisis and regulatory concerns of Basel II, credit risk assessment is becoming one of the most important topics in the field of financial risk management. Quantitative credit scoring models are widely used tools for credit risk assessment in financial institutions. Although single support vector machines (SVM) have been demonstrated with good performance in classification, a single classifier with a fixed group of training samples and parameters setting may have some kind of inductive bias. One effective way to reduce the bias is ensemble model. In this study, several ensemble models based on least squares support vector machines (LSSVM) are brought forward for credit scoring. The models are tested on two real world datasets and the results show that ensemble strategies can help to improve the performance in some degree and are effective for building credit scoring models.     

基于支持向量机的直接逆模型辨识

在简单讨论逆模型辨识原理的基础上,利用支持向量机(SVM)对函数逼近的能力,提出了基于支持向量机的直接逆模型辨识方法.分别采用二次核函数以及高斯RBF核函数,利用训练数据对线性和非线性系统进行黑箱辨识.仿真结果表明,基于支持向量机的直接逆模型辨识方法在处理线性和非线性对象时,辨识性能都优于传统的BP神经网络,不仅辨识精度高,辨识速度快,而且泛化能力较强.     

集成学习中基于离散化方法的基分类器构造研究

为构造集成学习中具有差异性的基分类器,提出基于数据离散化的基分类器构造方法,并用于支持向量机集成。该方法采用粗糙集和布尔推理离散化算法处理训练样本集,能有效删除不相关和冗余的属性,提高基分类器的准确性和差异性。实验结果表明,所提方法能取得比传统集成学习算法Bagging和Adaboost更好的性能。     

基于神经网络和层次SVM的多姿态人脸识别

提出了一种基于神经网络和层次支持向量机的多姿态人脸识别方法。该方法在训练阶段先利用神经网络把姿态人脸图像特征向准标准人脸图像特征映射，再根据聚类结果来训练支持向量机。识别阶段是利用神经网络变换得到待识别图像所对应的准标准图像的特征，再让层次支持向量机初步判断待识别图像最可能所属的人，最后利用否定算法对待识别的人脸图像进行确认。实验表明该算法效果较佳。     

Metamodeling development for reliability-based design optimization of automotive body structure

Metamodels are commonly used in reliability-based design optimization (RBDO) due to the enormously expensive computation cost of numerical simulations. However, for large-scale design optimization of automotive body structure, with the increasing number of design variable and enhanced nonlinearity degree of structural performance, polynomial response surface which is commonly used for vehicle design optimization often suffers exponentially increased computation burden and serious loss of approximation accuracy. In this paper, support vector regression, along with other four complex metamodeling techniques including moving least square, artificial neural network, radial basis function and Kriging, is investigated for approximating frontal crashworthiness performance which is one of the most highly nonlinear performances. It aims at testing support vector regression and providing advanced metamodeling technique for RBDO of automotive body structure. Approximation results are compared in both accuracy and computational efficiency. Based on the frontal crashworthiness example, it is found that support vector regression and moving least square are preferable techniques to approximate structural performances with good accuracy. But support vector regression is recommended for its computational efficiency and better approximation potential. Moreover, the ensemble of support vector regression, moving least square, Kriging and artificial neural network is an effective alternative and is proved, in the RBDO example for the lightweight design of front body structure, to outperform any other single metamodel. The remarkable predominance indicates that the ensemble of support vector regression, moving least square, Kriging and artificial neural network holds great potential in approximating highly nonlinear performances for RBDO of automotive body structure.     

Comments on "a parallel mixture of SVMs for very large scale problems"

Collobert, Bengio, and Bengio (2002) recently introduced a novel approach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the advantage that the required computation scales well to very large data sets. Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72% accuracy reported on an independent test set. Although this accuracy is impressive, their article does not consider alternative types of classifiers. We show that a simple ensemble of decision trees results in a higher accuracy, 94.75%, and is computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.     

基于聚类分析的综合神经网络集成算法

研究神经网络集成是一种有效实用的分类方法,权值是影响神经网络集成性能的重要因素。为了克服神经网络集成固定权值的缺陷,提出一种基于聚类分析的综合神经网络集成算法。算法首先将样本分类,每类样本中加入其他样本类一定数量的中心样本,不同的神经网络学习不同类的样本。根据输入数据与样本类别之间的相关程度自适应调整集成权值。算法不仅用于自适应调整集成权值,而且是一种产生个体神经网络的训练方法。四个数据集上的仿真试验证实了算法的有效性。