Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Twinning in Intermetallic Compounds Are Long Shear Vectors and/or Shuffles Really Necessary?

In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is revisited. A case study of mechanical twinning of HfV2+Nb, (cubic) Laves phase, is presented and the synchroshear of selected atomic layers is proposed to explain the physical process of twin formation. If the twins form in this way, then long shear vectors and / or atomicshuffles are not really necessary.

Construction and application of pre-classified smooth semi-supervised twin support vector machine

In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabeled samples. In S2TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S2TSVM (PS2TSVM) is proposed. Compared with S2TSVM, PS2TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS2TSVM is smoothed. After smoothing the model, the pre-classified smooth S2TSVM (PS3TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3TSVM model has better classification results.

Traffic Sign Recognition Based on CNN and Twin Support Vector Machine Hybrid Model

With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers.

Robust least squares projection twin SVM and its sparse solution

Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

基于特征加权混合隶属度的模糊孪生支持向量机

模糊孪生支持向量机(FTSVM)忽略了不同特征间的差异,导致核函数或距离的计算无法准确反映样本间的相似性,使FTSVM在处理含有大量不相关或弱相关特征的高维数据分类时,难以达到良好分类效果;且隶属度的设计未有效区分离群点或噪声。针对以上问题,提出了一种基于特征加权混合隶属度的FM-FTSVM。首先计算每个特征的信息增益,并依据信息增益值的大小为特征赋予权重,降低不相关或弱相关特征的作用,使其能更好地应用于高维数据分类;然后,为每一类样本构造一个最小包围球计算基于紧密度的特征加权隶属度,并结合基于距离的特征加权隶属度得到特征加权混合隶属度,综合考虑样本点到类中心的特征加权欧式距离和样本间的紧密程度,可更好识别离群点或噪声数据;最后,融合特征加权核函数,降低不相关特征对核函数或距离计算产生的影响。与对比算法在人工数据集、高维数据集和UCI数据集上进行比较,发现本文提出的方法在区分离群点、噪声和有效样本上有明显优势,且在高维数据集上可获得更好分类效果。

结构化最大间隔双支持向量机在股票预测中的应用

股票价格受政策、宏观经济以及公司经营状况等多方因素的影响,且各因素之间存在较高的相关性,因此股票数据存在的高噪声、非平稳等特性使得股票预测充满困难。为了减少数据中存在的噪声对股价预测准确性的影响,基于马氏距离的类间隔可分性,提出了结构化最大间隔双支持向量机,其分别针对正类样本和负类样本,寻找两个非平行的超平面,使每一类样本离本类样本的欧式距离尽可能小,同时离异类超平面的马氏距离尽可能大。8组基准数据集的实验结果表明,该方法在含噪声数据的分类问题上具有稳定的准确率,从而提升了模型的预测性能和抗噪能力。同时将其应用到股票涨跌趋势预测中,通过对上证综指、上证A指、上证380指数以及中国平安等14只股票实证分析的结果表明,相较于其他对比模型,结构化最大间隔双支持向量机表现出了较好的预测结果,具有一定的实用价值。

基于最小二乘孪生支持向量机的不确定数据学习算法

孪生支持向量机通过计算2个二次规划问题,得到2个不平行的超平面,用于解决二分类问题。然而在实际的应用中,数据通常包含不确定信息,这将会对构建模型带来困难。对此,提出了一种用于求解带有不确定数据的最小二乘孪生支持向量机模型。首先,对于每个实例,该方法都分配一个噪声向量来构建噪声信息。其次,将噪声向量结合到最小二乘孪生支持向量机,并在训练阶段得到优化。最后,采用一个2步循环迭代的启发式框架求解得到分类器和更新噪声向量。实验表明,跟其他对比方法比较,本方法采用噪声向量对不确定信息进行建模,并将孪生支持向量机的二次规划问题转化为线性方程,具有更好的分类精度和更高的训练效率。

两种抗病基因双T-DNA区双元载体的构建

选择标记基因的删除是植物转基因工程中重要的步骤之一,双T-DNA区双元载体转化法以其共整合频率高、删除标记基因效率高的优点在植物遗传转化中应用非常广泛。本文通过构建天麻抗真菌蛋白基因、脂质转移蛋白基因双T-DNA区双元载体,为培育不含选择标记基因的抗病新材料奠定基础。通过PCR的方法克隆GAFP基因,产物纯化测序确定序列准确后与pSB130-35S载体进行BamHⅠ、SacⅠ双酶切;pCAMBIA2300-LTP、pSB130-35S经EcoRⅠ、HindⅢ双酶切,酶切产物纯化后连接,连接产物转化大肠杆菌。采用基因特异性引物进行菌液PCR鉴定阳性克隆,重组质粒pSB130-GAFP、pSB130-LTP双酶切产物大小分别为540bp、1200bp,与预期产物大小一致,pSB130-GAFP、pSB130-LTP双T-DNA区双元载体构建成功。载体pSB130-GAFP、pSB130-LTP转化根癌农杆菌后可以直接用于植物的遗传转化。

Twin-SVM和Twin-KSVC标志物检测与分类方法

针对交通标志中禁令标志和指示标志的检测和分类难题,提出一种基于Twin-SVM和Twin-KSVC的交通标志检测与分类方法。对交通标志图像的红色、蓝色和亮度3个通道进行光照归一化处理;在这3个通道上提取Haar-like特征,构建特征向量;采用Twin-SVM方法进行交通标志检测过程的特征训练与验证,采用Twin-KSVC方法进行交通标志分类过程的特征训练与验证。实验采用实测数据对算法进行测试与评价,实验结果表明,该方法可以有效地检测和识别常见的20类禁令和指示交通标志。

基于混合孪生支持向量机的径流区间预测

径流具有非线性和随机性特征,单一点预测模型难以精确刻画和描述径流演化过程。为此,提出了一种可有效量化径流波动范围的智能区间预测方法。首先采用自适应噪声完备集合经验模态分解将非线性径流序列划分为若干子序列,并采用样本熵方法重构得到修正序列;其次以孪生支持向量机为基础,分别对复杂度较高的子序列构建区间预测模型、复杂度较低的子序列建立点预测模型,同时采用鲸鱼优化方法寻求满意的模型参数组合;最后将各子模型的预测结果叠加得到最终的预测区间。结果表明:所提方法具有良好的稳健性和可靠性,在点预测、区间预测等不同场景、不同预见期的性能指标均优于对比模型;如预见期为3 d时,对于黄河流域唐乃亥水文站,所得预测区间具有较高的可靠度与清晰度,其预测区间覆盖率PICP值为98.30%,预测区间平均宽度PINAW值为0.0792,可靠度、清晰度分别平均提高了9.47%和32.66%。研究成果可为智能化径流预测提供行之有效的方法。

基于传感信号采集的电控发动机振动故障监测方法

通过调理振动信号可以更高效地监测振动故障。为此,提出基于传感信号采集的电控发动机振动故障监测方法。首先,搭建电控发电机传感信号采集与处理架构,通过放大传感信号增益、滤波和转换信号模数的方式处理待监测信号,为提高监测准确性奠定可靠的数据基础。通过小波包分解与重构,获取信号的时域参数和小波能谱熵,并构建三维特征量。然后,利用“一对一”分解策略优化孪生支持向量机,构造多元分类器,使其更适用于振动故障监测这一多类别分类问题,再输入待监测信号的特征量,通过确定故障类别实现持续性监测。仿真结果表明:该方法训练耗时的最大值仅为897 ms,对于转子摩擦振动、不平衡振动等5种类型故障的监测准确率始终在97%以上,在缩减训练样本后准确率仍保持在90%以上。

应用高光谱技术及MLSPTSVM模型检测热损伤大豆

进口大豆在运输过程中极易因储藏温度过高而造成热损伤,加剧大豆蛋白及油脂的品质恶化,对大豆质量造成影响。利用高光谱图像技术和多元最小二乘递归投影孪生支持向量机(MLSPTSVM)对大豆的热损伤进行检测。应用高光谱图像采集系统在400~1000 nm范围内获取正常大豆、轻度热损伤、重度热损伤大豆的光谱图像。采用多种预处理方法进行光谱预处理,对预处理方法提高模型检测性能的有效性进行分析。结果表明,多元散射校正预处理搭配线性核的MLSPTSVM模型、原始光谱数据搭配非线性核的MLSPTSVM模型均能达到100%检测准确率,相较于经典检测模型具有显著优势。在实验样本数量大幅减少的情况下,应用线性核的模型检测准确率仍能达到100%。因此,结合MLSPTSVM模型的高光谱图像检测方法可有效地提高热损伤大豆检测精度,且具有良好的鲁棒性。

近邻密度辅助模糊优化孪生支持向量机的钢板表面缺陷分类

为提升钢板表面缺陷分类精度,提出一种选择性弱化样本的分类模型。首先,在图像预处理阶段引入显著性检测算法来减少二值化后图像出现失真的影响;其次,为了降低不利的边缘样本点对模型的影响,同时又能提高有利的边缘样本点对模型的贡献,构造了一种新的密度模糊隶属度函数对样本进行权重赋值;最后,在孪生支持向量机(TWSVM)的基础上,将构造的密度模糊隶属度函数作为优化条件嵌入模型内,提出了近邻密度辅助模糊优化的TWSVM算法,以提高分类效果。在数据集NEU上的实验结果表明,引入显著性检测算法后,重新设计的特征在整体准确率上提高了1.66%,同时采用优化后的算法进行缺陷分类,准确率达到98.33%,进一步提高了分类性能。

室内火灾高浓度烟雾环境火点增强识别仿真

火灾火点在形态上均具有不确定性,尤其在高浓度烟雾火灾环境中火点还具有随机透明度,易与图像的背景部分混合,导致火点位置识别难度较高。为此,提出室内火灾高浓度烟雾环境火点增强识别方法。对高浓度烟雾图像去噪、锐化以及分割处理,完成目标区域轮廓的提取。基于此,提取目标轮廓并对其增强,获取目标区域中火点的特征向量,结合孪生支持向量机对火点特征展开分类,实现室内火灾高浓度烟雾环境的火点的精准识别。实验结果表明,上述方法的火点识别精度高于98%,耗时低于210ms,且能够有效提取火点目标特征,证明了研究方法的应用效果更好,可靠性更高。

基于TSVM的燃机SFC整流晶闸管故障诊断

将燃机SFC整流晶闸管故障诊断抽象为多分类问题,提出了基于TSVM的分层多分类策略用于故障诊断、故障类型判别及故障定位。在燃机SFC整流桥机理研究及仿真分析基础上,归纳了故障诊断模型、故障类型判别模型及故障定位模型的输入参数。构建了SFC整流电路仿真模型,形成了22个案例,并调整触发角进行了共2200次仿真,获取了数据集用于模型检验。结果表明,所提出的策略在故障诊断、故障类型判别及故障晶闸管定位方面都取得了较高准确率,其可行性和有效性得到了充分验证。

Three-Dimensional Analysis of Rolling by Twin Shear Stress Yield Criterion

Using the twin shear stress yield criterion, the surface integral of the co-line vectors, and the integration depending on upper limit, Kobayashi＇s three-dimensional velocity field of rolling was analyzed and an analytical expression of rolling torque and single force was obtained. Through redoing the same experiment of rolling pure lead as Sims, the calculated results by the above expression were compared with those of Kobayashi and Sims formulae. The results show that the twin shear stress yield criterion is available for rolling analysis and the calculated results by the new formula are a little higher than those by Kobayashi and Sims ones if the reduction ratio is less than 30%.

具双T-DNA区的大麦黄矮病毒复制酶基因RNAi植物表达载体的构建(英文)

RNAi系双链RNA诱导同源mRNA降解,导致特定基因表达沉默的一种现象.RNAi技术是通过基因工程防治植物病毒病的最佳途径.由大麦黄矮病毒(Barely yellow dwarf virus,简称BYDV)引起的禾谷类黄矮病发病面积和危害程度在我国呈加重趋势.BYDV-GAV是当前流行的大麦黄矮病毒株系,本文针对BYDV-GAV复制酶基因序列,构建能在细胞内转录形成发卡RNA双链结构的表达盒,并将其置于中间载体pVec8-2b的T-DNA区,pVec8-2b的另一T-DNA区含有hpt选择报告基因表达盒.具双T-DNA区的病毒复制酶基因发卡RNA高效表达载体已导入根癌农杆菌,可用于诱导植物RNAi,创制无选择标记基因的抗大麦黄矮病转基因作物.

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

Review: Plant Binary Vectors of Ti Plasmid in <i>Agrobacterium tumefaciens</i>with a Broad Host-Range Replicon of pRK2, pRi, pSa or pVS1

This review chronicles the development of the plant binary vectors of Ti plasmid in Agrobacterium tumefaciens during the last 30 years. A binary vector strategy was designed in 1983 to separate the T-DNA region in a small plasmid from the virulence genes in avirulent T-DNA-less Ti plasmid. The small plant vectors with the T-DNA region have been simply now called binary Ti vectors. A binary Ti vector consist of a broad host-range replicon for propagation in A. tumeraciens, an antibiotic resistance gene for bacterial selection and the T-DNA region that would be transferred to the plant genome via the bacterial virulence machinery. The T-DNA region delimited by the right and left border sequences contains an antibiotic resistance gene for plant selection, reporter gene, and/or any genes of interest. The ColEI replicon was also added to the plasmid backbone to enhance the propagation in Escherichia coli. A general trend in the binary vector development has been to increase the plasmid stability during a long co-cultivation period of A. tumefaciens with the target host plant tissues. A second trend is to understand the molecular mechanism of broad host-range replication, and to use it to reduce the size of plasmid for ease in cloning and for higher plasmid yield in E. coli. The broad host-range replicon of VS1 was shown to be a choice of replicon over those of pRK2, pRi and pSA because of the superior stability and of small well-defined replicon. Newly developed plant binary vectors pLSU has the small size of plasmid backbone (4566 bp) consisting of VS1 replicon (2654 bp), ColE1 replicon (715 bp), a bacterial kanamycin (999 bp) or tetracycline resistance gene, and the T-DNA region (152 bp).