基于两阶段学习的半监督SVM故障检测方法

提出一种基于两阶段学习的半监督SVM故障检测方法。该方法首先使用标识传递算法给未标识样本赋予初始伪标识,并通过k近邻图对比样本点标识值,将可能是噪声的样本点识别并剔除;然后将去噪处理后的样本集输入到SVM中,使得SVM在训练时能兼顾整个样本集的信息,从而提高SVM的故障检测性能。实验中将该方法同支持向量机(SVM)、模糊支持向量机(FSVM)、直推式支持向量机(TSVM)及拉普拉斯支持向量机算法(LapSVM)进行比较,结果表明该方法在不同数目标识样本集合的情况下,检测精度较其他算法有较大幅度提高,同时该方法还比较了不含测试样本和含测试样本训练条件下的故障检测性能,结果表明结合测试样本可进一步提高算法的故障检测性能。     

基于文本挖掘的交互式专利分类

将文本挖掘理论应用于专利信息分析,提出了一种基于多分类器融合与主动学习的交互式专利分类算法,旨在实现高效的专利分类.该算法基于训练集,利用支持向量机,针对不同的专利类别分别训练相应的子分类器,然后通过多分类器融合对各子分类器进行有机结合,以获得性能更优的分类器和形成分类决策.在此基础上,利用主动学习选取最有信息的样本进行标引,从而通过人机交互实现分类模型的更新.针对传统批量选择性采样的缺点,还提出了动态批量选择性采样模式,通过确定度传播策略有效降低标引样本冗余度,以进一步提高主动学习的效率.实验结果表明,这种基于多分类器融合与主动学习的交互式专利分类算法的分类性能显著高于其他算法.     

一种新型回归支持向量机的学习算法

支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种具有很好泛化性能的回归方法,本文对标准支持向量机稍作改动,提出了一种新型回归支持向量机,并推导出它的对偶表达方式,随后利用一个优化定理设计了一个多变量更新学习算法,该算法能单调收敛于极值点,并具有简单的迭代方式,仿真实例说明所提出的回归支持向量机及其训练算法具有较好的学习性能.     

基于多视角二维主动学习的多标签分类

针对多标签图像分类问题的特点,提出了一种多视角二维主动学习(MV-2DAL)算法,以通过多视角学习与主动学习的有机结合,深入挖掘样本、标签、视角三个维度上的相关性和冗余性.此算法以样本-标签对作为基本标注单位,在每个视角内,利用二维主动学习的方法计算样本、标签维度上的不确定度;在不同视角间,通过多视角融合的方法计算跨视...     

基于支持向量机的步态分类方法

针对小样本步态数据引起的分类器泛化能力差的问题,提出了基于支持向量机的步态分类方法.采集了24名青年和24名老年受试者的步态数据,提取24个步态特征训练支持向量机,采用交叉验证方法评估分类器的泛化性能.结果表明,本文提出的方法能够有效地对小样本步态数据分类,并且具有良好的泛化性.不同的核函数对分类性能影响较小.与传统反向传播学习算法的神经网络分类器进行了比较,支持向量机分类性能明显优于传统反向传播学习算法的神经网络.支持向量机在步态分类中具有广泛的应用前景.     

基于曲率模态和支持向量机的结构损伤位置两步识别方法

支持向量机是一种基于统计学习理论的机器学习算法,能够较好的解决小样本的学习问题。介绍了支持向量机分类和回归算法,将其应用于梁结构的损伤诊断中。以曲率模态参数作为损伤识别指标,提出了基于支持向量机的结构损伤位置两步识别方法:首先根据支持向量机分类算法的概率估计找到可能的损伤位置,重新构造训练样本;然后利用支持向量机回归算法计算精确的损伤位置。通过对悬臂梁仿真计算进行了验证,结果表明:支持向量机在结构损伤诊断领域中具有较好的应用前景。     

支持向量机在电机转子故障诊断识别中的应用

提出了一种基于支持向量机的鼠笼式电机转子断条故障检测方法,通过对电机转子断条故障进行实验模拟,获取了采样信号,利用支持向量机(SVM)对故障样本进行训练,使得支持向量机(SVM)具有分类功能.最后,采用支持向量机(SVM)对电动机各种转子断条故障进行诊断分类,取得较满意的结果.     

基于动静态特征结合的改进模糊支持向量机行为识别

为了进一步提高行为识别的准确率,将视频中行为的动态特征和静态特征结合起来,应用一种改进的模糊支持向量机(FSVM)方法进行识别,该方法中采用一种新的隶属度确定方法,考虑了样本与类中心的距离以及样本与样本之间的紧密度关系;同时对支持向量机中靠近支持向量的难以识别的样本使用K近邻法识别.在KTH图像数据集上进行实验,将支持向量机与改进的模糊支持向量机两种识别方法进行比较,改进的模糊支持向量机在各类行为识别上取得了较高的识别率.     

不均衡数据下基于SVM的故障检测新算法

针对传统支持向量机(SVM)算法在数据不均衡情况下无法有效实现故障检测的不足,提出一种基于过抽样和代价敏感支持向量机相结合的故障检测新算法。该算法首先利用边界人工少数类过抽样技术(BSMOTE)实现训练样本的均衡。为减少人工增加样本带来的噪声影响,利用K近邻构造一个代价敏感的支持向量机(CSSVM)算法,利用每个样本的代价函数消除噪声样本对SVM算法分类精度的影响。将该算法应用在轴承故障检测中,并同传统的SVM算法,不同类代价敏感SVM-C算法,SVM和SMOTE相结合的算法进行比较,试验结果表明当样本不均衡时,建议算法的故障检测性能较其它算法有显著提高。     

基于支持向量机的网络风险评估方法的研究

统计学习理论(Statistical Learning Theory,SLT)是一种专门研究小样本情况下机器学习规律的理论,作为统计学习理论的VC维理论和结构风险最小化(Structure Risk Minimization,SRM)原则的具体实现算法支持向量机(support vector machinse,SVM),集优化、核(Kernel)、最佳推广能力等特点于一身,其出色的学习能力被广泛的关注并在各个领域广泛应用,系统介绍基于支持向量机的网络安全风险评估,给出其可行性、优越性及SVM评估模型,最后提出该研究发展方向与前景的预见。     

基于在线半监督学习的故障诊断方法研究

针对机械故障诊断中准确、完备的故障训练样本获取困难,而现有分类方法难以有效地发掘大量未标记故障样本中蕴含的有用信息,提出了一种基于在线半监督学习的故障诊断方法.该方法基于Tri-training算法将在线贯序极限学习机从监督学习模式扩展到半监督学习模式,利用少量不精确的标记样本构建初始分类器,并从大量未标记样本中在线扩充标记样本,对分类器进行增量式更新以提高其泛化性能.半监督基准数据试验结果表明,训练样本总数相同但标记样本数与未标记样本数比例不同时,所提算法得到的分类准确率相当且训练时间相差小于1.2倍.以柴油机8种工况的故障模式为对象进行试验验证,结果表明标记故障样本较少时,未标记故障样本的加入可使故障分类准确率提高5％～8％.     

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data, especially in the field of Synthetic Aperture Radar (SAR) image recognition. In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model, this paper proposes a semi-supervised learning method of threshold filtering. In the training process, through the two selections of data by the model, unlabeled data outside the category is filtered out to optimize the performance of the model. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and compared with existing several state-of-the-art semi-supervised classification approaches, the superiority of our method was confirmed, especially when the unlabeled data contained a large amount of out-of-category data.     

Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the proposed aspect2class annotation algorithm, we labeled bulk unlabeled tweets according to their contained aspect terms. However, we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets. To address this issue, we proposed the Volatile Stopwords Filtering (VSF) technique to reduce sparsity and enhance classifier performance. The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21% when using the random forest as a meta-classifier. Through testing on three benchmark datasets, we found that the SABAC ensemble framework performed exceptionally well. Our findings showed that international students during the pandemic faced various issues, including stress, uncertainty, health concerns, financial stress, and difficulties with online classes and returning to school. By analyzing and summarizing these annotated tweets, decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.     

Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need at present. In this paper, unsupervised learning DBSCAN combined with feature extraction of data has been used, and for some KPIs, its best F-Score can reach about 0.9, which is quite good for solving the current problem.     

数据特征选择与分类在机械故障诊断中的应用

针对机械故障数据的高维性和不平衡性,提出基于格拉斯曼流形的多聚类特征选择和迭代近邻过采样的故障分类方法。对采集到的振动信号,提取时域和频域相关特征,利用多聚类特征选择将高维数据以局部流形结构映射到低维特征集合。无标签样本借助迭代近邻过采样以恢复最大平衡性为目标进行样本分类,并对剩余无标签样本进行模糊分类。选取滚动轴承正常、外圈、内圈以及滚动体的故障数据,并与支持向量机、基于图的半监督学习算法进行对比。结果表明,提出的方法能有效识别出少数类故障,并在整体上有显著的分类效果。     

Outliers detection using an iterative strategy for semi‐supervised learning

As a direct consequence of production systems' digitalization, high‐frequency and high‐dimensional data has become more easily available. In terms of data analysis, latent structures‐based methods are often employed when analyzing multivariate and complex data. However, these methods are designed for supervised learning problems when sufficient labeled data are available. Particularly for fast production rates, quality characteristics data tend to be scarcer than available process data generated through multiple sensors and automated data collection schemes. One way to overcome the problem of scarce outputs is to employ semi‐supervised learning methods, which use both labeled and unlabeled data. It has been shown that it is advantageous to use a semi‐supervised approach in case of labeled data and unlabeled data coming from the same distribution. In real applications, there is a chance that unlabeled data contain outliers or even a drift in the process, which will affect the performance of the semi‐supervised methods. The research question addressed in this work is how to detect outliers in the unlabeled data set using the scarce labeled data set. An iterative strategy is proposed using a combined Hotelling's T² and Q statistics and applied using a semi‐supervised principal component regression (SS‐PCR) approach on both simulated and real data sets.     

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains small subsets to these issues. First, this research introduces a deep neural network (DNN)-based GANomaly for semi-supervised learning. Second, this paper presents the proposed adaptive algorithm for the DNN-based GANomaly, which is validated with four subsets of the adaptive dataset. Finally, this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations, reconstruction error visualization, and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage, semi-supervised learning, and adaptive learning. Compared to other single-class classification techniques, the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13% and 8% of F1 scores and 4.17% and 11.51% for accuracy, respectively. Furthermore, experiments of the proposed adaptive learning reveal mostly improved results over the initial values. An analysis and monitoring system based on the combination of the three explainable methodologies is also described. Thus, the proposed method has the potential advantages to be applied in practical industry, and future research will explore handling unbalanced real-time datasets in various scenarios.     

分布式包装实时数据库ARS算法应用

目的研究具有连续状态空间的复杂包装产品信息在分布式网络实时数据库中的查询方式。方法通过结合增强学习(EL)和自适应共振结构神经网络(ARS),给出一种基于增强学习的自适应共振结构神经网络算法——ELARS2。在ARS2算法中引入增强学习的选择和评估方式,解决在ARS2算法中分类模式的查询问题。设计在存储空间中使用分布式网络实时数据库查询目标的仿真试验,并用2种ELARS2算法(TDARS2和QARS2算法)来实现,并与经典的EL算法进行对比。结果 2种ELARS2算法完成查询目标的平均时间显著小于经典的EL算法。结论在2种ELARS2算法中,TDARS2比QARS2效果更好。     

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-of-the-art techniques.     

半监督极限学习机用于Android手机活动识别的研究

基于对现有Android手机活动识别技术的分析,针对从不完全、不充分的移动传感器数据中推断人体活动的难题,将能根据无标签样本提高识别预测准确性和速度的半监督(SS)学习和体现模式分类回归的有效学习机制的极限学习机(ELM)相结合给出了解决Android手机平台的人体活动识别问题的半监督极限学习机(SS-ELM)方法,并进一步提出了主成分分析(PCA)和半监督极限学习机(SS-ELM)结合的PCA+SS-ELM新方法。实验结果表明,该方法对人体活动的识别正确率能达到95%,优于最近提出的混合专家半监督模型的正确率,从而验证了该新方法是可行性。