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局部线性与one-class结合的科技文本分类方法

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计算机研究与发展 2005年第11期42卷 1862-1869页

作者：姚力群陶卿中国科学院自动化研究所北京100080 中国人民解放军炮兵学院二系合肥230031

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... 详细信息

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·

关键词：局部线性科技文献 one-class 文本分类

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

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Journal of Biomedical Science and Engineering 2010年第3期3卷 247-252页

作者： Malik Yousef Naim Najami Waleed Khalifav 不详

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.

关键词： MicroRNA one-class Two-class Machine Learning

Sequence Motif-Based one-class classifiers Can Achieve Comparable Accuracy to Two-class Learners for Plant microRNA Detection

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Journal of Biomedical Science and Engineering 2015年第10期8卷 684-694页

作者： Malik Yousef Jens Allmer Waleed Khalifa The Institute of Applied Research The Galilee Society Shefa Amr Israel Computer Science The College of Sakhnin Sakhnin Israel Molecular Biology and Genetics Izmir Institute of Technology Izmir Turkey Bionia Incorporated IZTEKGEB A8 Izmir Turkey

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.

关键词： microRNA one-class Plant Machine Learning

Fault Detection of Fuel Injectors Based on one-class classifiers

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Modern Mechanical Engineering 2014年第1期4卷 19-27页

作者： Dimitrios Moshou Athanasios Natsis Dimitrios Kateris Xanthoula-Eirini Pantazi Ioannis Kalimanis Ioannis Gravalos Agricultural Engineering Laboratory Aristotle University of Thessaloniki Thessaloniki Greece Department of Biosystems Engineering Technological Educational Institute of Thessaly Larissa Greece Department of Exploitation of Natural Resources and Agricultural Mechanics Agricultural University of Athens Athens Greece

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.

关键词： Fuel Injectors Fault Detection Acoustics Neural Networks one-class classifiers

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粗糙one-class支持向量机

计算机科学 2009年第9期36卷 242-245页

作者：王磊杨一帆周启海西南财经大学经济信息工程学院成都610074 西南财经大学中国支付体系研究中心成都610074

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... 详细信息

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。

关键词：粗糙集 one-class 支持向量机

融合连续域蚁群算法one-class SVM的电力离群用户检测

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国外电子测量技术 2020年第6期39卷 148-154页

作者：郭玮海南电网有限责任公司海口570203

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,... 详细信息

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。

关键词：蚁群算法 one-class SVM 离群检测电力离群

基于one-class SVM的机载塔康测距信息异常检测方法研究

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现代导航 2015年第3期6卷 282-285,309页

作者：李城梁中国电子科技集团公司第二十研究所西安710068

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... 详细信息

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。

关键词：异常检测机载塔康测距 one-class SVM

基于蚁群算法改进one-class SVM的电力离群用户检测算法研究

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自动化与仪器仪表 2019年第5期 111-114页

作者：黄宇腾裴旭斌孔历波李波殷杰国网浙江省电力有限公司信息通信分公司杭州310000 国网杭州供电有限公司杭州310000 浙江华云信息科技有限公司杭州310000

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... 详细信息

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。

关键词：蚁群算法 one-class SVM 离群检测电力离群

Online power quality disturbance detection by support vector machine in smart meter

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Journal of Modern Power Systems and Clean Energy 2019年第5期7卷 1328-1339页

作者： Imtiaz PARVEZ Maryamossadat AGHILI Arif I.SARWAT Shahinur RAHMAN Fahmida ALAM Department of Electrical and Computer Engineering Florida International UniversityMiamiUSA School of Computing and Information Sciences Florida International UniversityMiamiUSA

Power quality assessment is an important performance measurement in smart *** companies are interested in power quality monitoring even in the low level distribution side such as smart *** this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are *** the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal *** model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training *** the case of existence,the disturbances are further classified into different types such as sag,swell,transients and *** light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance *** continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

关键词： Machine learning one-class support vector machine Power quality Disturbances Smart grid Smart meter