Accurate performance estimators for information retrieval based on span bound of support vector machines

Support vector machines have met with significant success in the information retrieval field, especially in handling text classification tasks. Although various performance estimators for SVMs have been proposed, these only focus on accuracy which is based on the leave-one-out cross validation procedure. Information-retrieval-related performance measures are always neglected in a kernel learning methodology. In this paper, we have proposed a set of information-retrieval-oriented performance estimators for SVMs, which are based on the span bound of the leave-one-out procedure. Experiments have proven that our proposed estimators are both effective and stable.

Alcohols＇ Classification by Infrared Spectra Segment Based on Support Vector Machines

This paper studies various classifiers to identify primary, secondary or tertiary alcohols by using segmental spectra and their combinations to support vector machines （SVMs）. The results showed that the O-H in-plane bending absorption contributed most to identification their substitute. This conclusion disagrees with related known research results.

Using the Support Vector Machine Algorithm to Predict β-Turn Types in Proteins

The structure and function of proteins are closely related, and protein structure decides its function, therefore protein structure prediction is quite important.β-turns are important components of protein secondary structure. So development of an accurate prediction method ofβ-turn types is very necessary. In this paper, we used the composite vector with position conservation scoring function, increment of diversity and predictive secondary structure information as the input parameter of support vector machine algorithm for predicting theβ-turn types in the database of 426 protein chains, obtained the overall prediction accuracy of 95.6%, 97.8%, 97.0%, 98.9%, 99.2%, 91.8%, 99.4% and 83.9% with the Matthews Correlation Coefficient values of 0.74, 0.68, 0.20, 0.49, 0.23, 0.47, 0.49 and 0.53 for types I, II, VIII, I’, II’, IV, VI and nonturn respectively, which is better than other prediction.

The Comparison between Random Forest and Support Vector Machine Algorithm for Predicting β-Hairpin Motifs in Proteins

Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classi?cation

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine （IG-SVM） in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYLg, and GUCA2B.

Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model

This paper offers a new combined approach to predict and characterize β-turns in proteins.The approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a predictor.The first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in proteins.The second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation test.The SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,separately.Our results are compared with the previously best known β-turn prediction methods and are shown to give comparative performance.Most significantly,the SVM model provides some information related to β-turn residues in proteins.The results demonstrate that the present combination approach may be used in the prediction of protein structures.

Residuals-Based Deep Least Square Support Vector Machine with Redundancy Test Based Model Selection to Predict Time Series

In this paper, we propose a novel Residuals-Based Deep Least Squares Support Vector Machine(RBDLSSVM). In the RBD-LSSVM, multiple LSSVMs are sequentially connected. The second LSSVM uses the fitting residuals of the first LSSVM as input time series, and the third LSSVM trains the residuals of the second, and so on. The original time series is the input of the first LSSVM. Additionally, to obtain the best hyper-parameters for the RBD-LSSVM, we propose a model validation method based on redundancy test using Omni-Directional Correlation Function(ODCF). This method is based on the fact when a model is appropriate for a given time series, there should be no information or correlation in the residuals. We propose the use of ODCF as a statistic to detect nonlinear correlation between two random variables. Thus, we can select hyper-parameters without encountering overfitting,which cannot be avoided by only cross validation using the validation set. We conducted experiments on two time series: annual sunspot number series and monthly Total Column Ozone(TCO) series in New Delhi. Analysis of the prediction results and comparisons with recent and past studies demonstrate the promising performance of the proposed RBD-LSSVM approach with redundancy test based model selection method for modeling and predicting nonlinear time series.

Information acquisition technology for small hydropower stations in remote areas based on high-and low-orbit satellites

With a lack of coverage in private and public power communication networks,especially for collection of information from hydropower stations in remote areas,communication coverage is a significant issue.Satellite communication provides a large coverage area suitable for a variety of services and is less affected by geographical factors;moreover,the costs are independent of the communication distance.This study investigates information acquisition technology for small hydropower stations in remote areas using high-and low-orbit satellites.The information collection needs of small hydropower stations in remote areas are analyzed,and an information acquisition system is designed using high-and low-orbit satellites.For network security protection,network anomaly detection technology based on a support vector machine algorithm is proposed.The effectiveness of information collection was evaluated and verified for small hydropower plants in remote areas.The system provides technical support for“full coverage,full collection,and full monitoring”of the measurement automation information acquisition system.

Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM

In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.

ORDINAL REGRESSION FOR INFORMATION RETRIEVAL

This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effec- tiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM sig- nificantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.

DETECTION METHOD OF SPOT WELDING BASED ON MULTI-INFORMATION FUSION AND FRACTAL

A novel detection method of support vector machine （SVM） based on fractal dimension of signals is presented. And models of SVM are made based on nugget size defects of spot welding. Classification using these trained SVM models is done to signals of spot welding. It is shown from effect of different SVM models that these models with different inputs. In detection of defects, these models with inputs including sound signal have a high percentage of accuracy, the detection accuracy of these models with inputs including voltage signal will reduce. So the SVM models based on fractal dimensions of sound are some optimal nondestructive detection ones. At last a comparison between SVM detection model and ANNS detection model is researched which indicates that SVM is a more effective measure than Artificial neural networks in detection of nugget size defects during spot welding.

A Method of Using Information Entropy of an Image as an Effective Feature for Com-puter-Aided Diagnostic Applications

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. In general, a CAD system employs a classifier to detect or distinguish between abnormal and normal tissues on images. In the phase of classification, a set of image features and/or texture features extracted from the images are commonly used. In this article, we investigated the characteristic of the output entropy of an image and demonstrated the usefulness of the output entropy acting as a texture feature in CAD systems. In order to validate the effectiveness and superiority of the output-entropy-based texture feature, two well-known texture features, i.e., mean and standard deviation were used for comparison. The database used in this study comprised 50 CT images obtained from 10 patients with pulmonary nodules, and 50 CT images obtained from 5 normal subjects. We used a support vector machine for classification. A leave-one-out method was employed for training and classification. Three combinations of texture features, i.e., mean and entropy, standard deviation and entropy, and standard deviation and mean were used as the inputs to the classifier. Three different regions of interest (ROI) sizes, i.e., 11 × 11, 9 × 9 and 7 × 7 pixels from the database were selected for computation of the feature values. Our experimental results show that the combination of entropy and standard deviation is significantly better than both the combination of mean and entropy and that of standard deviation and mean in the case of the ROI size of 11 × 11 pixels (p < 0.05). These results suggest that information entropy of an image can be used as an effective feature for CAD applications.

Web Information Retrieval： Problem and Prospects

The information access is the rich data available for information retrieval, evolved to provide principle approaches or strategies for searching. For building the successful web retrieval search engine model, there are a number of prospects that arise at the different levels where techniques, such as Usenet, support vector machine are employed to have a significant impact. The present investigations explore the number of problems identified its level and related to finding information on web. The authors have attempted to examine the issues and prospects by applying different methods such as web graph analysis, the retrieval and analysis of newsgroup postings and statistical methods for inferring meaning in text. The proposed model thus assists the users in finding the existing formation of data they need. The study proposes three heuristics model to characterize the balancing between query and feedback information, so that adaptive relevance feedback. The authors have made an attempt to discuss the parameter factors that are responsible for the efficient searching. The important parameters can be taken care of for the future extension or development of search engines.

Keystroke Dynamics Based Authentication Using Information Sets

This paper presents keystroke dynamics based authentication system using the information set concept. Two types of membership functions (MFs) are computed: one based on the timing features of all the samples and another based on the timing features of a single sample. These MFs lead to two types of information components (spatial and temporal) which are concatenated and modified to produce different feature types. Two Component Information Set (TCIS) is proposed for keystroke dynamics based user authentication. The keystroke features are converted into TCIS features which are then classified by SVM, Random Forest and proposed Convex Entropy Based Hanman Classifier. The TCIS features are capable of representing the spatial and temporal uncertainties. The performance of the proposed features is tested on CMU benchmark dataset in terms of error rates (FAR, FRR, EER) and accuracy of the features. In addition, the proposed features are also tested on Android Touch screen based Mobile Keystroke Dataset. The TCIS features improve the performance and give lower error rates and better accuracy than that of the existing features in literature.

基于沙地猫群优化–最小二乘支持向量机的动态NOx排放预测

针对火电机组频繁调峰导致机组燃烧状态不稳,进而导致锅炉出口NOx浓度波动范围大的问题,提出一种基于沙地猫群优化(sand cat sarm optimization,SCSO)的最小二乘支持向量机(leastsquaressupportvectormachine,LSSVM) NOx动态预测模型。首先利用k近邻互信息计算时间延迟的同时筛选辅助变量。然后,基于SCSO算法进行输入变量阶次的选择。使用包含辅助变量时间延迟和阶次的信息作为模型的输入,SCSO算法优化最小二乘支持向量机参数,建立动态NOx排放最小二乘支持向量机预测模型(SCSO-LSSVM动态软测量模型)。最后将模型与未加入迟延的LSSVM模型,加入迟延的LSSVM模型和粒子群优化算法(particle swarm optimization,PSO)优化最小二乘支持向量机参数的动态软测量模型进行对比验证。结果表明,相较于其他模型,该文建立SCSO-LSSVM动态软测量模型均方根误差、平均绝对误差、平均绝对误差最小,预测精度最高,而且在NOx浓度剧烈波动时也能够较好地预测NOx浓度,具有很好的动态特性。

基于特征信息熵与支持向量机的智能网联汽车CAN总线异常检测技术

本文结合CNA报文的结构特点,探究了基于特征、信息熵的异常检测技术和基于支持向量机的异常检测技术。基于特征、信息熵的异常检测技术,将CAN ID作为特征,统计包含该特征的所有报文并计算信息熵。根据信息熵确立阈值标准,对比CAN总线报文的熵值是否在阈值范围内,从而检测是否存在异常。仿真结果表明,在报文数量较少的情况下,该技术的异常检测率可以达到100%。基于支持向量机的异常检测技术,将异常报文预处理后输入到支持向量机中训练,得到异常检测指标。利用该指标与CAN总线报文进行对比,从而检测是否存在异常。实验结果表明,该技术对多种CNA报文的异常检测率在90%以上。

基于ALIF-MPE-SVM组合算法的电机轴承早期故障诊断

为了提高电机用轴承的安全运行稳定效率,通过ALIF算法自适应分解非平稳信号,再以MPE从IMFs中提取出非线性故障信号,将MPE降维处理后的故障特征量利用MPE-SVM思想智能故障的诊断功能,开发得到一种MPE-SVM故障诊断技术,再根据测试得到的电机轴承故障参数完成算法有效性验证。研究结果表明:大部分故障信息都出现于最初的三个IMF内,主成分比例超过80%,因此以前3个主成分作为特征量并将其代入MPE-SVM内实施训练。各组别都可以对故障损伤的准确识别,表明以MPE作为故障特征能够满足有效性要求。ALIF-MPE具备比EMD-MPE更优的分类性能,达到了较低的标准差,稳定的分类状态。该研究能够准确识别电机轴承不同故障程度,对提高同类机械传动设备的故障诊断水平具有很好的理论支撑意义。

基于广域信息处理的配电网故障隔离技术研究

针对分布式电源并入配电网后,传统算法进行故障检测时存在定位准确度偏低、反应速度较慢的问题,文中基于广域信息处理技术提出了一种配电网故障隔离方法。该方法采用模态分解算法将故障复杂信号分解为多种类基础小信号,使用支持向量机对这些小信号进行数据分类。但由于传统支持向量机的收敛速度较慢,因此通过引入粒子群算法对其参数加以优化,从而提升模型的运算速度。实验结果表明,在加入分布式电源的电网中,所提算法的故障定位准确率为96.7%,平均运行时间则为43.9 s,且这两项参数在对比算法中均为最优。由此证明,该算法可应用于实际工程中,为配电网故障隔离提供技术支撑。

Estimation of tunnel axial orientation in the interlayered rock mass using a comprehensive algorithm

The axial selection of tunnels constructed in the interlayered soft-hard rock mass affects the stability and safety during construction.Previous optimization is primarily based on experience or comparison and selection of alternative values under specific geological conditions.In this work,an intelligent optimization framework has been proposed by combining numerical analysis,machine learning(ML)and optimization algorithm.An automatic and intelligent numerical analysis process was proposed and coded to reduce redundant manual intervention.The conventional optimization algorithm was developed from two aspects and applied to the hyperparameters estimation of the support vector machine(SVM)model and the axial orientation optimization of the tunnel.Finally,the comprehensive framework was applied to a numerical case study,and the results were compared with those of other studies.The results of this study indicate that the determination coefficients between the predicted and the numerical stability evaluation indices(STIs)on the training and testing datasets are 0.998 and 0.997,respectively.For a given geological condition,the STI that changes with the axial orientation shows the trend of first decreasing and then increasing,and the optimal tunnel axial orientation is estimated to be 87.This method provides an alternative and quick approach to the overall design of the tunnels.

基于支持向量机的油气生产复杂系统信息物理攻击识别方法

在数据驱动的复杂油气生产系统中,存在故障数据干扰攻击识别的问题,忽视系统内部可能存在的故障数据对攻击检测的影响,则难以及时防御攻击或解决故障。因此,为了提高复杂油气生产系统中信息物理攻击检测的准确性,提出了一种基于支持向量机的无向图联合检测方法。首先,对复杂油气生产系统中的关键传感器拓扑化形成无向图,建立传感器之间的连接关系并捕捉数据交互。然后,利用支持向量机检测传感器系统异常原因,并选择接收站低压泵及接收站储罐系统作为示例验证,前者的准确率、精确度、召回率和F1分数均高于99%,后者F1分数高于99%,其余均高于97%。与传统方法K均值聚类相比,本方法具有更高的准确性、鲁棒性和完整性,有助于防范攻击和生产事故,保障油气生产系统的安全。