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检索条件"主题词=Classification Algorithms"

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An outlier generation approach for one-class random forests: An example in one-class classification of remote sensing imagery

An outlier generation approach for one-class random forests:...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhongkui Shi Peijun Li Yi Sun SESS Peking University Beijing P R China

We propose a new outlier generation approach for one-class random forests (OCRF), a recently developed one-class classifier. The proposed method makes use of a positive and unlabeled learning (PUL) algorithm to generate outliers from the unlabeled samples. The outlier samples generated and the target samples are then used to train an OCRF classifier for one-class classification. The proposed method is evaluated using hyperspectral data, and the results showed that the OCRF with the proposed outlier generation method provides high classification accuracy, outperforming the original OCRF, PUL and OCSVM.

关键词： Roads classification algorithms Data mining Remote sensing Target recognition Histograms Biomedical imaging

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Enhancing Human-in-the-Loop Learning for Binary Sentiment Word classification

Enhancing Human-in-the-Loop Learning for Binary Sentiment Wo...

引用

IEEE Conference on Decision and Control

作者： Belen Martin-Urcelay Christopher J. Rozell Matthieu R. Bloch Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia U.S.A

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

While humans intuitively excel at classifying words according to their connotation, transcribing this innate skill into algorithms remains challenging. We present a human-guided methodology to learn binary word sentiment classifiers from fewer interactions with humans. We introduce a human perception model that relates the perceived sentiment of a word to the distance between the word and the unknown classifier. Our model informs the design of queries that capture more nuanced information than traditional queries solely requesting labels. Together with active learning strategies, our approach reduces human effort without sacrificing learning fidelity. We validate our method through experiments with human data, demonstrating improved accuracy in binary sentiment word classification.

关键词： Accuracy Active learning Human in the loop classification algorithms

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MMG-based classification of muscle activity for prosthesis control

MMG-based classification of muscle activity for prosthesis c...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： J. Silva W. Heim T. Chau Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto ONT Canada Rehabilitation Engineering Department Bloorview MacMillan Children's Centre Toronto ONT Canada

We have previously proposed the use of "muscle sounds" or mechanomyography (MMG) as a reliable alternative measure of muscle activity with the main objective of facilitating the use of more comfortable and functional soft silicone sockets with below-elbow externally powered prosthesis. This work describes an integrated strategy where data and sensor fusion algorithms are combined to provide MMG-based detection, estimation and classification of muscle activity. The proposed strategy represents the first ever attempt to generate multiple output signals for practical prosthesis control using a MMG multisensor array embedded distally within a silicon soft socket. This multisensor fusion strategy consists of two stages. The first is the detection stage which determines the presence or absence of muscle contractions in the acquired signals. Upon detection of a contraction, the second stage, that of classification, specifies the nature of the contraction and determines the corresponding control output. Tests with real amputees indicate that with the simple detection and classification algorithms proposed, MMG is indeed comparable to and may exceed EMG functionally.

关键词： Muscles Prosthetics Sockets Sensor fusion Distributed power generation Fusion power generation Signal generators Silicon Testing classification algorithms

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Contribution of Stereo Information to Feature-Based Pattern classification for Audio Semantic Analysis

Contribution of Stereo Information to Feature-Based Pattern ...

引用

International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： R. G. Kotsakis C. A. Dimoulas G. M. Kalliris Dept. of Journalism and Mass Communication Aristotle University of Thessaloniki Thessaloniki Greece

The current paper focuses on the evaluation of the contribution of stereo information to feature-based pattern classification for audio semantic analysis. In radio productions, where multiple speakers' voices and music coexist, it is quite important to detect voice and music patterns, in order to achieve efficient classification of the audio content. Recent research confirmed that the process of audio feature extraction and selection is critical for the overall system performance. In the current work, the feature extraction procedure is applied in each audio channel of stereo radio programmes separately in order to exploit the differences of audio information between channels. Feature ranking algorithms are employed, aiming to investigate the saliency of the feature differences. The classification performance of the implemented artificial neural systems revealed that the stereo differences have to be taken into consideration during audio semantic analysis.

关键词： Feature extraction Semantics Multimedia communication classification algorithms Multimedia databases Training Pattern classification

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A novel feature selection based on Tibetan grammar for Tibetan text classification

A novel feature selection based on Tibetan grammar for Tibet...

引用

IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Tao Jiang Hongzhi Yu State Key Laboratory of National Languages Information Technology Northwest Univercity for Nationalities Lanzhou Gansu P.R. China

ISBN: (纸本)9781479983544

Feature selection is a strategy that aims at making text classifiers more efficient and accurate. In this paper, we proposed a novel feature selection method based on Tibetan grammar for Tibetan classification. Tibetan language express grammatical meaning through the function words and word order, and the function word has large proportions. By analyzing the Tibetan grammar and distribution of part of speech, we proposed feature selection method based on Tibetan notional words. The method analyzed the part of speech of Tibetan text, and then used notional words as text features combined with IG method to realize feature selection. The experimental result shows that this method has improved significantly on classification efficiency and accuracy which compared with the traditional feature selection methods.

关键词： Text categorization Speech Grammar Semantics Accuracy classification algorithms Tagging

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sEMG feature selection and classification using SVM-RFE

sEMG feature selection and classification using SVM-RFE

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Mauricio C. Tosin Mariano Majolo Raissan Chedid Vinicius H. Cene Alexandre Balbinot Electrical Engineering Department Federal University of Rio Grande do Sul Porto Alegre Rio Grande do Sul Brazil

It is challenging to obtain good results for hand movements classification. Previous studies expended efforts on filters for sEMG data, feature extraction and classifier algorithms to achieve the best results. This paper proposes the insertion of a step in the classification process that selects which features to use in training aiming to increase accuracy and performance. Feature selection was previously used in other classification tasks but is new in wrist/fingers movements classification. Obtained results were positives as the performance gain is huge (39 to 53 features out of 144 are used for classification) and accuracy reach promising values (above 90% for some subjects).

关键词： Feature extraction Correlation classification algorithms Databases Training Frequency-domain analysis Noise measurement

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Efficient classification mechanism for network intrusion detection system based on data mining techniques: A survey

Efficient classification mechanism for network intrusion det...

引用

International Conference on (ISCO) Intelligent Systems and Control

作者： A. S Subaira P. Anitha Department of Computer Science & Engineering Dr. NGP Institute of Technology Coimbatore

In spite of growing information system widely, security has remained one hard-hitting area for computers as well as networks. In information protection, Intrusion Detection System (IDS) is used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining is an efficient artifice applied to intrusion detection to ascertain a new outline from the massive network data as well as it used to reduce the strain of the manual compilations of the normal and abnormal behavior patterns. This piece of writing reviews the present state of data mining techniques and compares various data mining techniques used to implement an intrusion detection system such as, Support Vector Machine, Genetic Algorithm, Neural network, Fuzzy Logic, Bayesian Classifier, K-Nearest Neighbor and decision tree algorithms by highlighting a advantage and disadvantages of each of the techniques.

关键词： Intrusion detection Data mining Decision trees classification algorithms Training Support vector machines Biological neural networks

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Integrating contour and skeleton for shape classification

Integrating contour and skeleton for shape classification

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Xiang Bai Wenyu Liu Zhuowen Tu Department of Electronics and Information Engineering Huazhong University of Science and Technology China Lab of Neuro Imaging University of California Los Angeles USA

Shape analysis has been a long standing problem in the literature. In this paper, we address the shape classification problem and make the following contributions: (1) We combine both contour and skeleton (also local and global) information for shape analysis, and we derive an effective classifier. (2) We collect a challenging shape database in which there are 20 categories of animals, with each having 100 shapes. All these shapes are obtained from real images with a large variation in pose, viewing angle, articulation, and self-occlusion. (3) We emphasize the importance of having good representation for shape classification to address the unique characteristics of shape. A thorough experimental study is conducted showing significant improvement by the proposed algorithm over many of the state-of-the-art shape matching and classification algorithms, on both our dataset and the well-known MPEG-7 dataset. In addition, we applied our algorithm for recognizing and classifying objects from natural images and obtained very encouraging results.

关键词： Skeleton Shape MPEG 7 Standard Information analysis Image analysis Image databases Animals classification algorithms Image recognition Conferences

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Graph classification Model Based on Graph Neural Networks and Graph Distance

Graph Classification Model Based on Graph Neural Networks an...

引用

International Conference on Computational Science/Intelligence and Applied Informatics (CSII)

作者： Xiaoling Li Zixuan Guan Qian Xu Shenze Huang Junxian Wang School of Information and Communication Engineering School of Computer and Cyberspace Security School of Animation and Digital Arts Communication University of China Beijing China

ISBN: (纸本)9781665402736

In recent years, multiple Graph Neural Network models have been proposed and applied to processing graph data and classification of graphs. However, when the graph data is limited, the model classification results are not satisfactory. In this paper, we propose a Graph Neural Network based model, which calculates the graph distance and Wasserstein's center of gravity on the base class tag, uses the K-Means method and Lloyd's algorithm, and gets the superclass by aggregation, which is called graph neural graph attention model (GNN-GAT). GIN is used as the feature extractor, and GNN network is used for classification. Secondly, we used MusicNtWrk to make two sets of graph datasets representing music mono-chord, downloaded the Triangles and Letter_high datasets, and imported these four kinds of datasets into the model for classification test and analysis.

关键词： Analytical models Computational modeling Feature extraction Graph neural networks Data models classification algorithms Informatics

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An IoT Security Attack classification Solution on the Perception Layer using Shallow Machine Learning

An IoT Security Attack Classification Solution on the Percep...

引用

Cryptography And Information Security (VCRIS), International Conference On

作者： Duc-Tho Mai Lam-Tra Nguyen Tran Thi Nga Duc-Cong Nguyen Thanh-Nam Pham The Academy of Cryptography Techniques (ACT) Hanoi Vietnam Thai Nguyen University of Information and Communication Technology Thai Nguyen Vietnam

ISBN: (数字)9798331529949

ISBN: (纸本)9798331529956

From the fifth-generation mobile communication systems (5G), the architecture of the Internet of Things (IoT) has grown enormously with more promising technologies and connections. However, edge realm components such as sensors and nodes still present a crucial security flaw related to radio frequency attacks. By restricting and stopping information from reaching the proper destination on the IoT's perception layer, jamming - a particular form of a Denial of Service (DoS) attack compromises the availability feature of IoT nodes. Finding and identifying these jammer attacks has been tricky because access to the network from nodes is also broken. To detect and categorize two kinds of jamming in the existing dataset, we propose to utilize three different Shallow Machine Learning (SML) architectures in this paper, including Decision Tree (DT), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), with the aims of fewer parameter usage, a low power-oriented architecture, and the capacity to handle real-time computer vision tasks. The experimental outcomes depict that SML is a promising approach since it can reach the best detection and classification accuracies (97% and 98.03%, respectively).

关键词： Support vector machines Radio frequency Machine learning algorithms Computer architecture Real-time systems classification algorithms Sensors Internet of Things Jamming Optimization

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