Least Squares One-Class Support Tensor Machine

One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods.

Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning

Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.

One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection.It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages,including noninvasiveness,user-friendliness,and social and legal acceptability.According to the literature,extensive research has been conducted on signature verification systems in a variety of languages,including English,Hindi,Bangla,and Chinese.However,the Arabic Offline Signature Verification(OSV)system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,diacritics,ligatures,and overlapping,making verification more difficult.Recently,signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries;however,performance on skilled forgery detection is still unsatisfactory.Most existing methods require many learning samples to improve verification accuracy,which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems.This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm(GA).In contrast to existing methods,which use multiclass learning approaches,this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification system.The proposed approach is tested on three signature databases(SID)-Arabic handwriting signatures,CEDAR(Center of Excellence for Document Analysis and Recognition),and UTSIG(University of Tehran Persian Signature),and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate(FAR),False Rejection Rate(FRR),and Equal Error Rate(ERR).The proposed system achieved 5%improvement.

基于one-class SVM与融合多可视化特征的可通行区域检测

针对难以获取完备的非可通行区域样本问题,为提高算法在不同场景的适应性,首次把可通行性检测看作单类分类问题,提出了基于one-class SVM的可通行区域检测算法.提出一种改进的融合颜色和纹理的特征提取方法,对各颜色分量进行离散余弦变换(DCT)变换,对DCT系数进行金字塔分解,用每个分解的均值和方差描述特征窗口.利用one-class SVM进行训练生成可通行区域的模式.实验表明,方法对新数据具有很好的识别能力,具有较高的检测精度和较低的误检率.

局部线性与One-Class结合的科技文本分类方法

结合了局部线性和One-Class的思想对科技文本分类问题进行了研究,利用局部线性的思想寻找文本样本的内在支撑流形,利用One-Class的思想确定正负样本的分界面·与K近邻算法、线性SVM算法和One-Class问题的SVM算法相比,给出的科技文本分类方法具有分类精度高、参数估计简便、正负样本分类精度可控制等优点,为解决科技文献的分类问题提供了一条有效的途径·

粗糙one-class支持向量机

粗糙集理论是处理不确定性和不完备信息的重要方法之一。通过将粗糙集理论引入到one-class支持向量机,提出了一种新颖的粗糙one-class支持向量机。通过定义上近似超平面和下近似超平面,使得训练样本能根据在粗糙间隔中的位置,自适应地对决策超平面产生影响。并且,outlier样本由于距离上近似超平面较近并产生较小的间隔误差,不会导致决策超平面对它们产生明显的过拟合。实验结果表明,粗糙one-class支持向量机的泛化性能优异,识别率和误识率均优于传统的one-class支持向量机。

η-one-class问题和η-outlier及其LP学习算法

用SVM方法研究one class和outlier问题 .在将one class问题理解为一种函数估计问题的基础上 ,作者首次定义了 η one class和 η outlier问题的泛化错误 ,进而定义了线性可分性和边缘 ,得到了求解one class问题的最大边缘、软边缘和ν 软边缘算法 .这些学习算法具有统计学习理论依据并可归结为求解线性规划问题 .算法的实现采用与boosting类似的思路 .实验结果表明该文的算法是有实际意义的 .

基于One-Class SVM的青鳉鱼异常行为识别方法

为了更准确地解析青鳉鱼在突发污染环境中的行为变化趋势,提出了一种基于One-Class SVM模型的青鳉鱼异常行为识别方法。以青鳉鱼的生理及行为特征作为观测指标,将采集到的暴露在不同类型和浓度特征污染物下的青鳉鱼行为强度信号作为经验数据,利用直方图统计和主成分分析(PCA)对行为强度数据进行降维,实现行为特征提取,基于One-Class SVM构建模型,并以五水合硫酸铜和三氯酚作为特征污染物进行暴露实验对算法进行验证。结果表明,One-Class SVM模型可以准确地识别正常行为和污染物暴露时发生的异常行为;对于有机污染物最快可在10 min内完成预警,重金属污染物可在1 h内完成预警,并且污染物浓度越高,模型的识别效果越好。识别方法可对水源突发性水质污染进行更有效的监测和预警,也可为水污染应急决策提供技术支撑。

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

Sequence Motif-Based One-Class Classifiers Can Achieve Comparable Accuracy to Two-Class Learners for Plant microRNA Detection

microRNAs (miRNAs) are short nucleotide sequences expressed by a genome that are involved in post transcriptional modulation of gene expression. Since miRNAs need to be co-expressed with their target mRNA to observe an effect and since miRNAs and target interactions can be cooperative, it is currently not possible to develop a comprehensive experimental atlas of miRNAs and their targets. To overcome this limitation, machine learning has been applied to miRNA detection. In general binary learning (two-class) approaches are applied to miRNA discovery. These learners consider both positive (miRNA) and negative (non-miRNA) examples during the training process. One-class classifiers, on the other hand, use only the information for the target class (miRNA). The one-class approach in machine learning is gradually receiving more attention particularly for solving problems where the negative class is not well defined. This is especially true for miRNAs where the positive class can be experimentally confirmed relatively easy, but where it is not currently possible to call any part of a genome a non-miRNA. To do that, it should be co-expressed with all other possible transcripts of the genome, which currently is a futile endeavor. For machine learning, miRNAs need to be transformed into a feature vector and some currently used features like minimum free energy vary widely in the case of plant miRNAs. In this study it was our aim to analyze different methods applying one-class approaches and the effectiveness of motif-based features for prediction of plant miRNA genes. We show that the application of these one-class classifiers is promising and useful for this kind of problem which relies only on sequence- based features such as k-mers and motifs comparing to the results from two-class classification. In some cases the results of one-class are, to our surprise, more accurate than results from two-class classifiers.

基于One-Class SVM的机载塔康测距信息异常检测方法研究

针对多源导航信息融合系统中导航传感器数据保障的问题,本文提出了一种基于One-Class SVM的机载塔康测距信息异常检测方法。首先,提取机载塔康测距信息的时域参数构成特征样本空间;然后,采用One-Class SVM训练出机载塔康测距信息正常状态时的模型,通过发现非正常状态的样本进行异常检测。利用模拟的机载塔康测距数据进行方法验证,实验结果表明:该异常检测方法对机载塔康测距信息中的噪声有一定的鲁棒性,可以满足实际应用的需要。

Prediction of miRNA Based on miRNA Biogenesis via One-class SVM

MicroRNAs are a class of small, single-stranded RNAs which are produced by non-protein-coding RNA genes with a length of 21-29 nt. They regulate the expression of protein-encoding genes at the post-transcriptional level and the degradation ofmRNAs by base pairing to mRNAs. Mature miRNAs are processed from 60-90 nt RNA hairpin structures called pre-miRNAs. At present, most of the machine learning computational methods for pre-miRNAs prediction are based on two-class SVM and use structural information of pre-miRNA hairpins. Those methods share a common feature that all of them need a negative dataset in the training dataset and feature selection in both training and testing dataset. In order to avoid selecting false negative examples of miRNA hairpins in the training dataset which may mislead the classifiers, we presented a microRNA prediction algorithm called MirBio based on miRNAs Biogenesis which is trained only on the information of the positive miRNAs class to predict miRNAs. It can predict both pre-miRNAs and miRNAs and get a relatively satisfying result in this study.

融合连续域蚁群算法One-Class SVM的电力离群用户检测

连续域蚁群优化算法是蚁群优化算法的主要研究方向。通过分析蚁群觅食过程中的位置分布与食物来源之间的关系,提出了蚁群一类支持向量机(One-Class SVM)算法。在此算法的基础上,设计了一种电力离群用户检测算法,给出了算法的求解形式,根据高维用电负荷数据的特点,提出了一种基于改进One-Class SVM算法的电力离群用户检测方法,同时采用蚁群算法对支持向量机的训练参数进行优化,可以在样本分布不均匀、样本分布未知的环境下有效识别电力离群用户,并对其他算法的测试结果进行了比较和分析,以验证所提出算法的正确性和有效性。

A comparison study between one-class and two-class machine learning for MicroRNA target detection

The application of one-class machine learning is gaining attention in the computational biology community. Different studies have described the use of two-class machine learning to predict microRNAs (miRNAs) gene target. Most of these methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present a study using one-class machine learning for miRNA target discovery and compare one-class to two-class approaches. Of all the one-class methods tested, we found that most of them gave similar accuracy that range from 0.81 to 0.89 while the two-class naive Bayes gave 0.99 accuracy. One and two class methods can both give useful classification accuracies. The advantage of one class methods is that they don’t require any additional effort for choosing the best way of generating the negative class. In these cases one- class methods can be superior to two-class methods when the features which are chosen as representative of that positive class are well defined.

Fault Detection of Fuel Injectors Based on One-Class Classifiers

Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To overcome these circumstances, various condition monitoring techniques can be applied. The application of acoustic signals is common in the field of fault diagnosis of rotating machinery. Advanced signal processing is utilized for the construction of features that are specialized in detecting fuel injector faults. A performance comparison between novelty detection algorithms in the form of one-class classifiers is presented. The one-class classifiers that were tested included One-Class Support Vector Machine (OCSVM) and One-Class Self Organizing Map (OCSOM). The acoustic signals of fuel injectors in different operational conditions were processed for feature extraction. Features from all the signals were used as input to the one-class classifiers. The one-class classifiers were trained only with healthy fuel injector conditions and compared with new experimental data which belonged to different operational conditions that were not included in the training set so as to contribute to generalization. The results present the effectiveness of one-class classifiers for detecting faults in fuel injectors.

Development of one-class classification method for identifying healthy T.granosa from those contaminated with uncertain heavy metals by LIBS

Laser-induced breakdown spectroscopy(LIBS)can be used for the rapid detection of heavy metal contamination of Tegillarca granosa(T.granosa),but an appropriate classification model needs to be constructed.In the one-class classification method,only target samples are needed in training process to achieve the recognition of abnormal samples,which is suitable for rapid identification of healthy T.granosa from those contaminated with uncertain heavy metals.The construction of a one-class classification model for heavy metal detection in T.granosa by LIBS has faced the problem of high-dimension and small samples.To solve this problem,a novel one-class classification method was proposed in this study.Here,the principal component scores and the intensity of the residual spectrum were combined as extracted features.Then,a one-class classifier based on Mahalanobis distance using the extracted features was constructed and its threshold was set by leave-one-out crossvalidation.The sensitivity,specificity and accuracy of the proposed method were reached to 1,0.9333 and 0.9667 respectively,which are superior to the previously reported methods.

融合自编码器和one-class SVM的异常事件检测

目的在自动化和智能化的现代生产制造过程中,视频异常事件检测技术扮演着越来越重要的角色,但由于实际生产制造中异常事件的复杂性及无关生产背景的干扰,使其成为一项非常具有挑战性的任务。很多传统方法采用手工设计的低级特征对视频的局部区域进行特征提取,然而此特征很难同时表示运动与外观特征。此外,一些基于深度学习的视频异常事件检测方法直接通过自编码器的重构误差大小来判定测试样本是否为正常或异常事件,然而实际情况往往会出现一些原本为异常的测试样本经过自编码得到的重构误差也小于设定阈值,从而将其错误地判定为正常事件,出现异常事件漏检的情形。针对此不足,本文提出一种融合自编码器和one-class支持向量机(support vector machine,SVM)的异常事件检测模型。方法通过高斯混合模型(Gaussian mixture model,GMM)提取固定大小的时空兴趣块(region of interest,ROI);通过预训练的3维卷积神经网络(3D convolutional neural network,C3D)对ROI进行高层次的特征提取;利用提取的高维特征训练一个堆叠的降噪自编码器,通过比较重构误差与设定阈值的大小,将测试样本判定为正常、异常和可疑3种情况之一;对自编码器降维后的特征训练一个one-class SVM模型,用于对可疑测试样本进行二次检测,进一步排除异常事件。结果本文对实际生产制造环境下的机器人工作场景进行实验,采用AUC(area under ROC)和等错误率(equal error rate,EER)两个常用指标进行评估。在设定合适的误差阈值时,结果显示受试者工作特征(receiver operating characteristic,ROC)曲线下AUC达到91.7%,EER为13.8%。同时,在公共数据特征集USCD(University of California,San Diego)Ped1和USCD Ped2上进行了模型评估,并与一些常用方法进行了比较,在USCD Ped1数据集中,相比于性能第2的方法,AUC在帧级别和像素级别分别提高了2.6%和22.3%;在USCD Ped2数据集中,相比于性能第2的方法,AUC在帧级别提高了6.7%,从而验证了所提检测方法的有效性与准确性。结论本文提出的视频异常事件检测模型,结合了传统模型与深度学习模型,使视频异常事件检测结果更加准确。

面向协作机器人的零力控制与碰撞检测方法研究

在3C(计算机、通信和消费电子)行业中,对协作机器人的安全、交互、精度、柔顺等方面有严格要求。为了解决协作机器人柔顺交互控制问题,对机器人的零力控制和碰撞检测方法进行了深入研究。首先,建立了一种分析冗余协作机器人牛顿−穆尔彭罗斯(Newton−MP)通用的逆运动学算法,将逆运动学问题转化为Newton−MP法的迭代求解问题。其次,针对协作机器人的零力控制问题,通过同时考虑摩擦力形成完整动力学方程。同时,建立基于加速度3次摩擦力模型的完全动力学方程,采用遗传算法对摩擦力模型进行多参数辨识。再次,提出基于One-class卷积神经网络的碰撞检测方法,构建无碰撞数据集。One-class卷积神经网络在特征空间中引入伪负高斯数据,并使用2元交叉熵损失对网络进行了训练。One-class卷积神经网络碰撞检测方法成功地补偿了模型不确定的动态影响,解决了传统碰撞检测方法建模不准确的问题。最后,通过实验证明,提出的Newton−MP优化方法具有良好的性能,绝对误差达到0.00013 mm。与理想摩擦力模型进行对比,采用基于速度的3次摩擦力模型拟合出的摩擦力能够更好适用于零力控制。将外力矩观测器与One-class卷积神经网络碰撞检测进行优缺点分析,可以证明,One-class卷积神经网络可以在不依靠模型的情况下,准确地检测机器人的异常碰撞。

Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific nodes.This study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node information.The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet metadata.This makes it well-suited for direct application in IoT environments due to its low resource consumption.In experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based features.Additionally,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM.Furthermore,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling methods.This study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.

One-Class Support Vector Machine with Relative Comparisons

One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative comparisons. The analysis uses a hypersphere version of one-class SVMs with a penalty term appended to the objective function. The method simultaneously finds the minimum sphere in the feature space that encloses most of the target points and considers the relative comparisons. The result is a standard convex quadratic programming problem, which can be solved by adapting standard methods for SVM training, i.e., sequential minimal optimization. This one-class SVM can be applied to semi-supervised clustering and multi-classification problems. Tests show that this method achieves higher accuracy and better generalization performance than previous SVMs.