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Anomaly-Aware Peripheral Blood Smear Analysis via Hybrid Detection and One-Class Learning 认领 引用
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作者 Issac Neha Margret Rajakumar Krishnan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1208-1244,共37页
Background:Examining peripheral blood smears is a vital diagnostic tool in hematology.Although deep learning-based automated systems have improved the accuracy of blood cell detection,most current methods depend on fu... Background:Examining peripheral blood smears is a vital diagnostic tool in hematology.Although deep learning-based automated systems have improved the accuracy of blood cell detection,most current methods depend on fully supervised learning and need extensive annotations to identify unusual cell shapes.Clinical practice often lacks these complete annotations,limiting the ability to generalize to rare or unseen abnormalities.To address this issue of incomplete annotated data,this paper introduces a hybrid framework that recognizes anomalies for reliable and clear analysis of peripheral blood smears.Methods:The proposed framework combines supervised blood cell detection with unsupervised one-class anomaly detection.We use a YOLOv8 detector to accurately identify and categorize red blood cells,white blood cells,and platelets in peripheral blood smear images.The detected blood cells are then assessed further using a lightweight Cell Reconstruction Autoencoder,which is trained only on normal-looking cells.To enhance sensitivity to minor variations in the model's construction,we also introduce a Wasserstein-based method for latent distribution regularization.This method scores anomalies based on how well the reconstruction matches and the deviation in the latent space,without needing any abnormal training samples.Results:We evaluate detection performance on the TXL-PBC dataset and validate it externally with pathological samples from the ASH Image Bank.The results show high accuracy in detecting abnormalities and in distinguishing normal from abnormal cells(F1Score/Anomaly=0.926;AUC=0.963).Our experiments and statistics confirm that using the hybrid approach with Wasserstein regularization significantly improves sensitivity and robustness compared to individual or reconstruction model-based detection methods.Conclusions:The hybrid YOLOv8-Cell Reconstruction Autoencoder framework accurately identifies cells and detects previously unseen morphological abnormalities within a one-class learning framework.This approach combines efficiency with anomaly awareness and clear clinical interpretation,making it suitable for practical and scalable analysis of peripheral blood smears in clinical settings. 展开更多
关键词 Peripheral blood smear analysis object detection one-class learning deep learning blood cell detection you only look once
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HADAR-UAV:Risk-Calibrated One-Class Learning Framework for Zero-Day Intrusion Detection in Unmanned Aerial Vehicle Networks 认领 引用
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作者 Canan Batur Şahin Siti Fatimah Abdul Razak +2 位作者 Arif Ullah Ali Fatih Gündüz Nazri Mohd Nawi 《Computers, Materials & Continua》 SCIE EI 2026年第10期1282-1304,共23页
Unmanned Aerial Vehicle(UAV)networks face escalating cybersecurity threats,especially from zero-day attacks that exploit previously unknown vulnerabilities.To address this,we present HADAR-UAV(Hybrid Anomaly Detection... Unmanned Aerial Vehicle(UAV)networks face escalating cybersecurity threats,especially from zero-day attacks that exploit previously unknown vulnerabilities.To address this,we present HADAR-UAV(Hybrid Anomaly Detection with Adaptive Risk-calibration for UAV).This novel intrusion detection framework integrates masked autoencoder representation learning with Deep Support Vector Data Description(Deep SVDD)under conformal prediction guarantees to calibrate risk.Our method overcomes three critical limitations of existing approaches:(i)over-reliance on attack signatures,(ii)lack of statistical guarantees on false alarm rates,and(iii)insufficient robustness in feature extraction under partial observation.Using a rigorous Leave-Two-Attack-Families-Out(L2AFO)evaluation protocol on the UAVIDS-2025 benchmark,HADAR-UAV achieves strong zero-day detection—0.997±0.001 ROC-AUC and 0.992±0.002 F1-Score—while empirically maintaining a target false alarm rate through conformal calibration applied to deterministic scores.All results are reported as mean±standard deviation across 20 independent runs(5 seeds×4 folds)and show statistically significant improvement(paired t-test,p<0.01)over current one-class methods.Ablation studies confirm that every architectural component adds measurable value to the framework.Additional cross-dataset validation on NSL-KDD under a one-class zero-day-inspired setting further indicates that the proposed framework generalizes beyond MAVLink-specific traffic patterns. 展开更多
关键词 UAV cybersecurity intrusion detection zero-day attacks one-class classification masked auto encoder
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Predicting Marine Fuels with Unusual Wax Appearance Temperatures Using One-Class Support Vector Machines 认领 引用
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作者 Njideka Chima-Amaeshi Chris O’Malley Mark Willis 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第6期1208-1217,共10页
Accurate and robust detection of wax appearance(a medium-to high-molecular-weight component of crude oil)is crucial for the efficient operation of hydrocarbon transportation.The wax appearance temperature(WAT)is the l... Accurate and robust detection of wax appearance(a medium-to high-molecular-weight component of crude oil)is crucial for the efficient operation of hydrocarbon transportation.The wax appearance temperature(WAT)is the lowest temperature at which the wax begins to form.When crude oil cools to its WAT,wax crystals precipitate,forming deposits on pipelines as the solubility limit is reached.Therefore,WAT is a crucial quality assurance parameter,especially when dealing with modern fuel oil blends.In this study,we use machine learning via MATLAB’s Bioinformatics Toolbox to predict the WAT of marine fuel samples by correlating near-infrared spectral data with laboratory-measured values.The dataset provided by Intertek PLC-a total quality assurance provider of inspection,testing,and certification services-includes industrial data that is imbalanced,with a higher proportion of high-WAT samples compared to low-WAT samples.The objective is to predict marine fuel oil blends with unusually high WAT values(>35℃)without relying on time-consuming and irregular laboratory-based measurements.The results demonstrate that the developed model,based on the one-class support vector machine(OCSVM)algorithm,achieved a Recall of 96,accurately predicting 96%of fuel samples with WAT>35℃.For standard binary classification,the Recall was 85.7.The trained OCSVM model is expected to facilitate rapid and well-informed decision-making for logistics and storage when choosing fuel oils. 展开更多
关键词 Marine fuel One-class support vector machines Wax appearance temperature Wax Machine learning
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基于One-Class学习的鲁棒音频真伪识别 认领 引用
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作者 梁子琪 张旭龙 +1 位作者 王健宗 肖京 《大数据》 2025年第3期167-187,共21页
深度伪造技术对社会经济、政治稳定和社会安全构成了严重威胁,而深度伪造中,语音伪造技术被广泛应用于电话诈骗、舆论操控等危害性活动中。近年来,随着深度学习技术的应用,语音合成和语音转换技术飞速进步,已经能够生成以假乱真的语音,... 深度伪造技术对社会经济、政治稳定和社会安全构成了严重威胁,而深度伪造中,语音伪造技术被广泛应用于电话诈骗、舆论操控等危害性活动中。近年来,随着深度学习技术的应用,语音合成和语音转换技术飞速进步,已经能够生成以假乱真的语音,足以欺骗机器和人类。针对语音伪造技术的危害,目前已经有许多语音欺骗检测技术来提高说话人验证系统的可靠性。然而,现有方法往往依赖于已知攻击类型的先验知识,在面对未知攻击类型的先验知识时,其泛化能力受到限制。基于One-Class学习构建了一个语音欺骗检测系统,通过为真实语音建立严格的决策边界,将边界外的样本判定为伪造语音,从而增强了模型的泛化能力。此外,针对伪造语音数据稀缺的问题,引入具有更强通用性和鲁棒性的自监督模型Wav2vec2进行特征提取,进一步提高了模型在面对未知类型先验知识攻击时的识别准确率。实验结果表明,提出的方法在保证良好语音鉴伪性能的同时,减少了CM系统对下游ASV系统的潜在干扰,有效解决了伪造语音数据稀缺和模型泛化能力不足的问题。 展开更多
关键词 伪造检测 One-Class学习 自监督学习
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Least Squares One-Class Support Tensor Machine 认领 引用
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作者 Kaiwen Zhao Yali Fan 《Journal of Computer and Communications》 2024年第4期186-200,共15页
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 ... 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. 展开更多
关键词 Least Square One-Class Support Tensor Machine One-Class Classification Upscale Least Square One-Class Support Vector Machine One-Class Support Tensor Machine
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基于One-Class SVM的青鳉鱼异常行为识别方法 认领 引用 被引量：9
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作者 罗毅 王伟 +9 位作者 刘勇 姜杰 刘翠棉 赵乐 李歆琰 李治国 廖日红 王艳 王新春 饶凯锋 《河北工业科技》 CAS 2022年第3期230-236,共7页
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... 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。 展开更多
关键词 环境质量监测与评价 模式识别 青鳉鱼 异常行为 One-Class SVM
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融合自编码器和one-class SVM的异常事件检测 认领 引用 被引量：16
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作者 胡海洋 张力 李忠金 《中国图象图形学报》 CSCD 北大核心 2020年第12期2614-2629,共16页
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... 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。 展开更多
关键词 视频异常事件检测 时空兴趣块 3维卷积神经网络 降噪自编码器 one-class支持向量机
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Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine 认领 引用 被引量：3
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作者 HU Lei HU Niaoqing +1 位作者 QIN Guojun GU Fengshou 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第3期474-479,共6页
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.T... 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. 展开更多
关键词 novelty detection condition monitoring incremental clustering one-class support vector machine turbopump
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局部线性与One-Class结合的科技文本分类方法 认领 引用 被引量：4
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作者 姚力群 陶卿 《计算机研究与发展》 EI 北大核心 2005年第11期1862-1869,共8页
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... 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· 展开更多
关键词 局部线性 科技文献 One-Class 文本分类
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粗糙one-class支持向量机 认领 引用 被引量：2
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作者 王磊 杨一帆 周启海 《计算机科学》 北大核心 2009年第9期242-245,共4页
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... 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。 展开更多
关键词 粗糙集 one-class 支持向量机
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η-one-class问题和η-outlier及其LP学习算法 认领 引用 被引量：1
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作者 陶卿 齐红威 +1 位作者 吴高巍 章显 《计算机学报》 EI CAS 北大核心 2004年第8期1102-1108,共7页
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 ... 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 . 展开更多
关键词 one-class问题 outlier 最大边缘 统计学习理论 支持向量机 线性规划问题 boosting
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ESSENTIAL RELATIONSHIP BETWEEN DOMAIN-BASED ONE-CLASS CLASSIFIERS AND DENSITY ESTIMATION 认领 引用 被引量：2
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作者 陈斌 李斌 +1 位作者 冯爱民 潘志松 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第4期275-281,共7页
One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of t... One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships. 展开更多
关键词 one-class support vector machine（OCSVM） support vector data description（SVDD） kernel density estimation
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Prediction of miRNA Based on miRNA Biogenesis via One-class SVM 认领 引用
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作者 LIU Yuan-ning YAN Wen +3 位作者 ZHANG Hao LI Zhi LU Hui-jun LI Xin 《Chemical Research in Chinese Universities》 SCIE CAS 2010年第5期803-809,共7页
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 lev... 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. 展开更多
关键词 miRNAs Hairpin One-class classification miRNAs Biogenesis
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Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning 认领 引用 被引量：8
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作者 Wentao Mao Gangsheng Wang +1 位作者 Linlin Kou Xihui Liang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第2期524-546,共23页
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-c... 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. 展开更多
关键词 Anomaly detection domain adaptation domainadversarial training one-class classification transfer learning
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基于one-class SVM与融合多可视化特征的可通行区域检测 认领 引用 被引量：2
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作者 高华 赵春霞 韩光 《机器人》 EI CSCD 北大核心 2011年第6期731-735,741,共5页
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... 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. 展开更多
关键词 可通行区域检测 one-class SVM 多可视化特征 自主导航
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基于One-Class SVM的机载塔康测距信息异常检测方法研究 认领 引用
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作者 李城梁 《现代导航》 2015年第3期282-285,309,共4页
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... 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。 展开更多
关键词 异常检测 机载塔康测距 One-Class SVM
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基于蚁群算法改进One-Class SVM的电力离群用户检测算法研究 认领 引用 被引量：3
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作者 黄宇腾 裴旭斌 +2 位作者 孔历波 李波 殷杰 《自动化与仪器仪表》 2019年第5期111-114,共4页
用电采集负荷数据反映了用户的用电特性及用电习惯,通过用电负荷数据分析识别用电离群用户在工业生产中具有重要意义。本文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... 用电采集负荷数据反映了用户的用电特性及用电习惯,通过用电负荷数据分析识别用电离群用户在工业生产中具有重要意义。本文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。通过对某市纺织业用户的数据进行实践证明,改进的算法能够有效提高收敛速度,并有效地识别离群的用电用户。 展开更多
关键词 蚁群算法 One-Class SVM 离群检测 电力离群
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One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features 认领 引用
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作者 Ansam A.Abdulhussien Mohammad F.Nasrudin +1 位作者 Saad M.Darwish Zaid A.Alyasseri 《Computers, Materials & Continua》 SCIE EI 2023年第4期219-242,共24页
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 com... 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. 展开更多
关键词 Offline signature verification biometric system feature fusion one-class classifier
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A comparison study between one-class and two-class machine learning for MicroRNA target detection 认领 引用 被引量：2
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作者 Malik Yousef Naim Najami Waleed Khalifav 《Journal of Biomedical Science and Engineering》 2010年第3期247-252,共6页
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 targ... 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. 展开更多
关键词 MicroRNA One-Class Two-Class Machine Learning
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融合连续域蚁群算法One-Class SVM的电力离群用户检测 认领 引用 被引量：1
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作者 郭玮 《国外电子测量技术》 2020年第6期148-154,共7页
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,... 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。 展开更多
关键词 蚁群算法 One-Class SVM 离群检测 电力离群
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