Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm

Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and 86.5% lipid tissue. The results show that this method can be a considerable way for semi-automatic segmentation of atherosclerotic plaque components in clinical IVOCT images.     

基于支持向量机的室颤信号检测算法

目的:实现室颤信号与非室颤信号的分类,进而实现室颤信号的检测。方法:本文引入了一种基于支持向量机(Support Vec-tor Machine,SVM)和改进的越限区间算法(TCI)的新算法,其中支持向量机在处理分类和模式识别等问题中具有很大的优势。该算法采用4s的滑动窗技术,并利用改进后的越限区间算法(Threshold Crossing Interval,TCI)方法提取心电信号的特征。新算法的实现如下:在每一滑动窗内采用改进的后的绝对值阈值,计算中间2s内的平均越限间隔值。并以此TCI值作为特征参数,输入一个预先设计好的二分类支持向量机中,从而实现分类。结果:成功实现了室颤信号的检测,通过计算该方法的灵敏度、精确度、预测性和准确度且与其他方法相比较,可知此新算法总体可靠性优于其他方法。结论:该算法能够实现室颤信号的实时监测,且简单易行,易于实现,较适合实时的心电监测以及除颤仪器。     

支持向量机在害虫发生量预测中的应用

害虫发生量与其影响因子之间具有复杂的非线性和时滞性关系,传统方法不能很好的分析和拟合高度非线性的害虫发生量变化规律,导致预测精度不理想。为了有效构建害虫发生量与其影响因子之间复杂的非线性关系模型,提高害虫发生量预测精度,提出一种基于支持向量机的害虫发生量预测方法。该方法首先通过F测验对害虫发生量的最佳时滞阶数进行确定,并利用最佳时滞阶数对样本进行重构;然后利用前向浮动因子筛选法对害虫发生量的影响因子进行筛选,筛选出对预测结果贡献大的影响因子;最后采用10折交叉验证得到害虫发生量的最优预测模型。采用粘虫的幼虫发生密度数据在Mat-lab7.0平台下对该方法进行测试与分析,实验结果表明,相对于其它预测方法,支持向量机提高了害虫发生量的预测精度,克服了传统方法的缺陷,更适合于非线性、小样本的害虫发生量预测。     

基于支持向量机分类的RNA共同二级结构预测

比较序列分析作为RNA二级结构预测的最可靠途径, 已经发展出许多算法。将基于此方法的结构预测视为一个二值分类问题: 根据序列比对给出的可用信息, 判断比对中任意两列能否构成碱基对。分类器采用支持向量机方法, 特征向量包括共变信息、热力学信息和碱基互补比例。考虑到共变信息对序列相似性的要求, 通过引入一个序列相似度影响因子, 来调整不同序列相似度情况下共变信息和热力学信息对预测过程的影响, 提高了预测精度。通过49组Rfam-seed比对的验证, 显示了该方法的有效性, 算法的预测精度优于多数同类算法, 并且可以预测简单的假节。     

一种基于支持向量机的自适应肿瘤分类检测算法

根据肿瘤分类检测模型的特点,提出了一种新的算法,该算法结合使用了基因选择和数据抽取的有效方法,并在此基础上使用支持向量机对基因表达数据进行分类或者检测。其中乳腺癌的分类交叉验证结果由88．46％提高到100．0％,急性白血病的也由71．05％提高至100．0％。实验结果说明了这一方法的有效性,为在大量的基因表达数据中提高检测癌症的准确性提出了一种比较通用的方法。     

A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.     

基于支持向量机的枯草杆菌启动子预测方法

启动子预测是研究基因转录调控的重要环节,但现有算法的预测正确率偏低．在深入分析启动子生物特征的基础上,提出了一种基于支持向量机的枯草杆菌启动子预测算法,在启动子序列的组成特征、信号特征和结构特征中选取9种典型特征作为预测的依据,对于信号特征,除了利用保守模式的一致序列,还考虑了间隔距离的分布信息．首先通过特征描述模型分别计算每种特征在启动子序列和非启动子序列中的得分,将特征得分组合成9维特征向量,再利用支持向量机在特征向量集上进行训练和判别．对实际数据集进行的刀切法测试验证了算法的有效性．对σ启动予的预测,平均正确率达到了90．7％;对几种其它σ因子启动子的预测,平均正确率也超过了80％．算法不但有广泛的适用性,还有良好的可扩展性,能够方便的容纳新特征,使识别性能不断提高．     

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.     

基于模糊支持向量机的膜蛋白折叠类型预测

现有的基于支持向量机（support vector machine,SVM）来预测膜蛋白折叠类型的方法．利用的蛋白质序列特征并不充分．并且在处理多类蛋白质分类问题时存在不可分区域,针对这两类问题．提取蛋白质序列的氨基酸和二肽组成特征,并计算加权的多阶氨基酸残基指数相关系数特征,将3类特征融和作为分类器的输入特征矢量．并采用模糊SVM（fuzzy SVM,FSVM）算法解决对传统SVM不可分数据的分类．在无冗余的数据集上测试结果显示．改进的特征提取方法在相同分类算法下预测性能优于已有的特征提取方法：FSVM在相同特征提取方法下性能优于传统的SVM．二者相结合的分类策略在独立性数据集测试下的预测精度达到96．6％．优于现有的多种预测方法．能够作为预测膜蛋白和其它蛋白质折叠类型的有效工具．     

支持向量机方法预测蛋白质结构中的二硫键

在蛋白质结构预测的研究中,一个重要的问题就是正确预测二硫键的连接,二硫键的准确预测可以减少蛋白质构像的搜索空间,有利于蛋白质3D结构的预测,本文将预测二硫键的连接问题转化成对连接模式的分类问题,并成功地将支持向量机方法引入到预测工作中。通过对半胱氨酸局域序列连接模式的分类预测,可以由蛋白质的一级结构序列预测该蛋白质的二硫键的连接。结果表明蛋白质的二硫键的连接与半胱氨酸局域序列连接模式有重要联系,应用支持向量机方法对蛋白质结构的二硫键预测取得了良好的结果。     

Using Support Vector Machine Ensembles for Target Audience Classification on Twitter

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.     

Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix

Lipid–protein interactions play a vital role in various biological processes, which are involved in cellular functions and can affect the stability, folding and the function of peptides and proteins. In this study, a sequence-based method by using support vector machine and position specific scoring matrix (PSSM) was proposed to predict lipid-binding sites. Considering the influence of surrounding residues of one amino acid, a sliding window was chosen to encode the PSSM profiles. By incorporating the evolutionary information and the local features of residues surrounding one lipid-binding site, the method yielded a high accuracy of 80.86% and the Matthew’s Correlation Coefficient of 0.58 by using fivefold cross validation test. The good result indicates the applicability of the method.     

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine

Background

Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions.

Results

Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species.

Conclusions

The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided.     

基于支持向量机的细菌基因组水平转移基因预测

随着各种生物基因组序列测定工作的完成,大量的DNA序列数据涌现出来,为研究在基因组中寻找水平转移基因提供了极大的便利.将基因序列特征分析和支持向量机技术结合起来,通过分析基因序列的特征差异发现水平转移基因.依据以前研究工作的基础,选取了绝对密码子使用频率(FCU)作为序列特征,主要因为它既包含了基因密码子使用偏性的信息,也包含了基因所编码蛋白的氨基酸组成信息,支持向量机利用这些信息进行水平转移基因分析和预测,可以提高预测的准确性.另外,提出了基于分链的水平转移基因预测新方法,即将细菌基因组前导链和滞后链上的基因区别对待,分别进行水平转移基因预测.结果显示,基本预测方法要优于目前预测结果最好的Tsirigos等提出的基于八联核苷酸频率的打分算法,命中率的相对提高率最高达31.47%,而基于分链的方法对水平转移基因的预测取得了更好的结果.     

A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.     

苏云金杆菌杀虫晶体蛋白活性预测的支持向量机模型

藉均匀设计(UD)方法,构建了苏云金杆菌(Bt)杀虫晶体蛋白氨基酸组成特征与其杀虫活性之间关系的支持向量机(SVM)模型。当惩罚系数为0·01、epsilon值为0·2、gamma值为0·05、域值为0·5时,该模型对Bt杀虫晶体蛋白杀虫活性的预测平均准确率达73%。     

一种基于支持向量机技术的癫痫脑电棘尖波识别方法

支持向量机是一种基于统计学习理论的新型学习机。文章提出一种基于支持向量机的癫痫脑电特征提取与识别方法,充分发挥其泛化能力强的特点,在与神经网络方法的比较中,表现出较低的漏检率和较好的鲁棒性,有深入研究的价值和良好的应用前景。     

支持向量机与神经网络的关系研究

支持向量机是一种基于统计学习理论的新颖的机器学习方法,由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点,该方法已经广泛用于解决分类和回归问题．本文将结构风险函数应用于径向基函数网络学习中,同时讨论了支持向量回归模型和径向基函数网络之间的关系．仿真实例表明所给算法提高了径向基函数网络的泛化性能．     

A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.