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运用分类学习器探讨肩三针治疗586例卒中后肩手综合征的显效率影响因素 被引量:14
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作者 崔韶阳 罗晓舟 +3 位作者 何家扬 张笑 邓博文 唐纯志 《针刺研究》 CAS CSCD 北大核心 2018年第11期733-737,共5页
目的:采用分类学习器对586例卒中后肩手综合征患者的病历资料进行机器学习,探讨患者的证候体征对显效率的影响,尝试总结提高临床治疗本病显效率的可行方法。方法:从病历系统中提取符合纳入条件的肩三针治疗卒中后肩手综合征患者的病例资... 目的:采用分类学习器对586例卒中后肩手综合征患者的病历资料进行机器学习,探讨患者的证候体征对显效率的影响,尝试总结提高临床治疗本病显效率的可行方法。方法:从病历系统中提取符合纳入条件的肩三针治疗卒中后肩手综合征患者的病例资料,运用单规则(1R)学习器、RIPPER算法学习器及C 5.0决策树模型对所搜集资料进行机器学习。结果:学习结果显示,疾病分期、面色差异、舌苔差异、血压等级、是否饮酒、体质量指数(BMI)及患者是否吸烟是对本法治疗卒中后肩手综合征的显效率影响较大的因素。结论:面色、舌质、血压、饮酒及吸烟习惯、BMI等是影响肩三针治疗卒中后肩手综合征显效率的主要因素,临床医生可以在治疗或对患者的宣教中加以重视。 展开更多
关键词 分类学习器 肩三针 肩手综合征
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基于叶尖定时技术的轮盘外缘裂纹故障分类方法研究
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作者 汤成 《黑龙江科学》 2024年第16期116-119,共4页
针对轮盘外缘裂纹信号的故障分类需求,设计了基于Simulink的叶尖定时信号仿真模型,利用MATLAB对不同裂纹程度进行分类。通过ANSYS有限元分析软件建立叶片轮盘系统模型,计算不同转速下叶片的固有频率,得到叶片动频与转速频率的函数关系,... 针对轮盘外缘裂纹信号的故障分类需求,设计了基于Simulink的叶尖定时信号仿真模型,利用MATLAB对不同裂纹程度进行分类。通过ANSYS有限元分析软件建立叶片轮盘系统模型,计算不同转速下叶片的固有频率,得到叶片动频与转速频率的函数关系,根据叶片振动理论,利用Simulink设计叶片振动系统仿真模型,考虑转子转速波动和不平衡振动对叶片振动的影响,建立对应的仿真模型,得到含扰动量的叶尖定时信号。对叶尖定时信号进行频谱分析,通过Simulink设计数字滤波器,去除信号中含扰动量的频率成分,构造不同裂纹程度的叶尖定时信号数据集,将其去噪后导入MATLAB分类学习器中,对不同分类方法进行对比并测试。结果表明,此方法可对不同裂纹程度的叶尖定时信号进行准确分类,为轮盘外缘裂纹故障分类方法研究提供参考。 展开更多
关键词 叶尖定时 有限元分析 SIMULINK仿真 频谱分析 分类学习器 故障诊断
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语音签到系统中的信号处理技术 被引量:1
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作者 李金龙 原立格 《电子产品世界》 2023年第7期80-85,共6页
研究了快速准确签到的语音签到系统,该系统利用MATLAB仿真对语音信号进行处理并提取特征量进行分类,使其满足语音签到的目的,主要从信号滤波、信号检测、信号处理、分类学习等方面体现信号处理的整体过程。用MATLAB中的分类学习器模块... 研究了快速准确签到的语音签到系统,该系统利用MATLAB仿真对语音信号进行处理并提取特征量进行分类,使其满足语音签到的目的,主要从信号滤波、信号检测、信号处理、分类学习等方面体现信号处理的整体过程。用MATLAB中的分类学习器模块对特征变量通过不同的模型,并对比不同模型来找到最适合语音信号的分类模型,以达到识别语音签到者的目的。 展开更多
关键词 信号滤波 端点检测 信号处理 分类学习器
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An Accurate and Extensible Machine Learning Classifier for Flow-Level Traffic Classification 被引量:2
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作者 Gang Lu Ronghua Guo +1 位作者 Ying Zhou Jing Du 《China Communications》 SCIE CSCD 2018年第6期125-138,共14页
Machine Learning(ML) techniques have been widely applied in recent traffic classification.However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In... Machine Learning(ML) techniques have been widely applied in recent traffic classification.However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset,we apply SMOTE algorithm in generating artificial training samples for minority classes.We evaluate our classifier on two datasets collected from different network border routers.Compared with the previous multi-class traffic classifiers built in one-time training process,our classifier achieves much higher F-Measure and AUC for each application. 展开更多
关键词 traffic classification class imbalance dircriminator bias encrypted traffic machine learning
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Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors 被引量:6
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作者 Xiujuan Wang Chenxi Zhang Kangfeng Zheng 《China Communications》 SCIE CSCD 2016年第7期24-31,共8页
Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic fire... Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection. 展开更多
关键词 intrusion detection DCNN density cluster center nearest neighbor
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Research on internet traffic classification techniques using supervised machine learning 被引量:1
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作者 李君 Zhang Shunyi +1 位作者 Wang Pan Li Cuilian 《High Technology Letters》 EI CAS 2009年第4期369-377,共9页
Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emer... Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emerged applications (e. g. Peer-to-Peer) using dynamic port numbers, masquerading techniques and encryption to avoid detection. This paper presents a machine learning (ML) based traffic classifica- tion scheme, which offers solutions to a variety of network activities and provides a platform of performance evaluation for the classifiers. The impact of dataset size, feature selection, number of application types and ML algorithm selection on classification performance is analyzed and demonstrated by the following experiments: (1) The genetic algorithm based feature selection can dramatically reduce the cost without diminishing classification accuracy. (2) The chosen ML algorithms can achieve high classification accuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexity and accuracy are both taken into account. (3) Larger dataset and fewer application types would result in better classification accuracy. Finally, early detection with only several initial packets is proposed for real-time network activity and it is proved to be feasible according to the preliminary results. 展开更多
关键词 supervised machine learning traffic classification feature selection genetic algorithm (GA)
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Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models 被引量:2
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作者 Zaijian Wang Yuning Dong +1 位作者 Shiwen Mao Xinheng Wang 《China Communications》 SCIE CSCD 2017年第10期202-218,共17页
To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi... To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method. 展开更多
关键词 dictionary learning traffic classication multimedia traffic K-singular value decomposition quality of service
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Automatic malware classification and new malware detection using machine learning 被引量:10
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作者 Liu LIU Bao-sheng WANG +1 位作者 Bo YU Qiu-xi ZHONG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第9期1336-1347,共12页
The explosive growth ofmalware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect... The explosive growth ofmalware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware pro- grams. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import fimctions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the un- known malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware. 展开更多
关键词 Malware classification Machine learning N-GRAM Gray-scale image Feature extraction Malware detection
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Discovering optimal features using static analysis and a genetic search based method for Android malware detection 被引量:7
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作者 Ahmad FIRDAUS Nor Badrul ANUAR +1 位作者 Ahmad KARIM Mohd Faizal Ab RAZAK 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第6期712-736,共25页
Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, an... Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search(GS), which is a search based on a genetic algorithm(GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Na?ve Bayes(NB), functional trees(FT), J48, random forest(RF), and multilayer perceptron(MLP). Among these classifiers, FT gave the highest accuracy(95%) and true positive rate(TPR)(96.7%) with the use of only six features. 展开更多
关键词 Genetic algorithm Static analysis ANDROID MALWARE Machine learning
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Two-level hierarchical feature learning for image classification 被引量:3
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作者 Guang-hui SONG Xiao-gang JIN +1 位作者 Gen-lang CHEN Yan NIE 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2016年第9期897-906,共10页
In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific... In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods. 展开更多
关键词 Transfer learning Feature learning Deep convolutional neural network Hierarchical classification Spectral clustering
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A new constrained maximum margin approach to discriminative learning of Bayesian classifiers 被引量:1
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作者 Ke GUO Xia-bi LIU +2 位作者 Lun-hao GUO Zong-jie LI Zeng-min GENG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第5期639-650,共12页
We propose a novel discriminative learning approach for Bayesian pattern classification, called 'constrained maximum margin (CMM)'. We define the margin between two classes as the difference between the minimum de... We propose a novel discriminative learning approach for Bayesian pattern classification, called 'constrained maximum margin (CMM)'. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con- straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un- constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach. 展开更多
关键词 Discriminative learning Statistical modeling Bayesian pattern classifiers Gaussian mixture models UCI datasets
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Performance analysis of new word weighting procedures for opinion mining 被引量:2
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作者 G.R.BRINDHA P.SWAMINATHAN B.SANTHI 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2016年第11期1186-1198,共13页
The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative ... The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting(IWW). IWW is computed based on the significance of the word in the document(SWD) and the significance of the word in the expression(SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed:(1) Classification performance is enhanced;(2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy. 展开更多
关键词 Inferred word weight Opinion mining Supervised classification Support vector machine(SVM) Machine learning
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Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests
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作者 Cristian GODOY Ivan DEPINA Vikas THAKUR 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2020年第6期445-461,共17页
Geotechnical classification is vital for site characterization and geotechnical design.Field tests such as the cone penetration test with pore water pressure measurement(CPTu)are widespread because they represent a fa... Geotechnical classification is vital for site characterization and geotechnical design.Field tests such as the cone penetration test with pore water pressure measurement(CPTu)are widespread because they represent a faster and cheaper alternative for sample recovery and testing.However,classification schemes based on CPTu measurements are fairly generic because they represent a wide variety of soil conditions and,occasionally,they may fail when used in special soil types like sensitive or quick clays.Quick and highly sensitive clay soils in Norway have unique conditions that make them difficult to be identified through general classification charts.Therefore,new approaches to address this task are required.The following study applies machine learning methods such as logistic regression,Naive Bayes,and hidden Markov models to classify quick and highly sensitive clays at two sites in Norway based on normalized CPTu measurements.Results showed a considerable increase in the classification accuracy despite limited training sets. 展开更多
关键词 Machine learning CLASSIFICATION Quick clays Sensitive clays
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Affective rating ranking based on face images in arousal-valence dimensional space
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作者 Guo-peng XU Hai-tang LU +1 位作者 Fei-fei ZHANG Qi-rong MAO 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第6期783-795,共13页
In dimensional affect recognition, the machine learning methods, which are used to model and predict affect, are mostly classification and regression. However, the annotation in the dimensional affect space usually ta... In dimensional affect recognition, the machine learning methods, which are used to model and predict affect, are mostly classification and regression. However, the annotation in the dimensional affect space usually takes the form of a continuous real value which has an ordinal property. The aforementioned methods do not focus on taking advantage of this important information. Therefore, we propose an affective rating ranking framework for affect recognition based on face images in the valence and arousal dimensional space. Our approach can appropriately use the ordinal information among affective ratings which are generated by discretizing continuous annotations.Specifically, we first train a series of basic cost-sensitive binary classifiers, each of which uses all samples relabeled according to the comparison results between corresponding ratings and a given rank of a binary classifier. We obtain the final affective ratings by aggregating the outputs of binary classifiers. By comparing the experimental results with the baseline and deep learning based classification and regression methods on the benchmarking database of the AVEC 2015 Challenge and the selected subset of SEMAINE database, we find that our ordinal ranking method is effective in both arousal and valence dimensions. 展开更多
关键词 Ordinal ranking Dimensional affect recognition VALENCE AROUSAL Facial image processing
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