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检索条件"主题词=CLASSIFICATION algorithms"

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A k-nearest neighbor based algorithm for multi-label classification

A k-nearest neighbor based algorithm for multi-label classif...

引用

IEEE International Conference on Granular Computing (GRC)

作者： Min-Ling Zhang Zhi-Hua Zhou National Laboratory for Novel Software Technology Nanjing University Nanjing China

In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi-label lazy learning approach named ML-kNN is presented, which is derived from the traditional k-nearest neighbor (kNN) algorithm. In detail, for each new instance, its k-nearest neighbors are firstly identified. After that, according to the label sets of these neighboring instances, maximum a posteriori (MAP) principle is utilized to determine the label set for the new instance. Experiments on a real-world multi-label bioinformatic data show that ML-kNN is highly comparable to existing multi-label learning algorithms.

关键词： classification algorithms Text categorization Bioinformatics Nearest neighbor searches Decision trees Kernel Testing Machine learning Laboratories Proteins

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Feature extraction using PCA for VHR satellite image time series spatio-temporal classification

Feature extraction using PCA for VHR satellite image time se...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： S. Réjichi F. Chaabane Carthage University Sup'Com COSIM laboratory Tunisia

Image feature extraction is a challenging task as it directly affects analysis of Satellite Image Time Series (SITS) which tackles a huge amount of information (spatial and spectral resolution increase). Therefore, in this paper, Principle Component Analysis (PCA) is applied for feature extraction to improve a multitemporal classification approach for Very High Resolution (VHR) SITS. The improved multitemporal classification succeeds to discern between regions behaviors (stable, periodic etc.), which is very useful in land cover monitoring. Experimental tests have been conducted on both synthesized and real SITS. Performance comparison between PCA and Fisher Feature Selection (Fisher-FS) algorithms is established.

关键词： Feature extraction Principal component analysis Support vector machines Satellites classification algorithms Kernel Time series analysis

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A parallel SVM training algorithm on large-scale classification problems

A parallel SVM training algorithm on large-scale classificat...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jian-Pei Zhang Zhong-Wei Li Jing Yang College of Computer Science and Technology Harbin Engineering of Technology Harbin China

Support vector machine (SVM) has become a popular classification tool but the main disadvantages of SVM algorithms are their large memory requirement and computation time to deal with very large datasets. To speed up the process of training SVM, parallel methods have been proposed by splitting the problem into smaller subsets and training a network to assign samples of different subsets. A parallel training algorithm on large-scale classification problems is proposed, in which multiple SVM classifiers are applied and may be trained in a distributed computer system. As an improvement algorithm of cascade SVM, the support vectors are obtained according to the data samples' distance mean and the feedback is not the whole final output but alternating to avoid the problem that the learning results are subject to the distribution state of the data samples in different subsets. The experiment results on real-world text dataset show that this parallel SVM training algorithm is efficient and has more satisfying accuracy compared with standard cascade SVM algorithm in classification precision.

关键词： Support vector machines Support vector machine classification classification algorithms Large-scale systems Quadratic programming Concurrent computing Training data Distributed computing Machine learning Educational institutions

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Computer-Aided classification of Breast Tumors Using the Affinity Propagation Clustering

Computer-Aided Classification of Breast Tumors Using the Aff...

引用

The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Yanni Su Yuanyuan Wang Department of Electronic Engineering Fudan University Shanghai 200433 China

To improve the accuracy and sensitivity of the breasttumor classification based on ultrasound images, a computer-aidedclassification algorithm is proposed using the AffinityPropagation (AP) clustering. Five morphologic features and threetexture features are extracted from each breast ultrasoundimage. The AP clustering with an empirical value of "preference"is used as the primary classification to classify tumors into fiveclusters with each cluster enjoying specific and similar featuresand holding a certain probability to be malignant. Then, fiveclusters are further classified into benign and malignantaccording to their feature distribution. The proposed system isvalidated by experiments of 132 cases (including 67 benigntumors and 65 malignant ones) with its performance comparedwith those of popular methods such as the back propagationartificial neural network (ANN), the self-organizing mappingANN and the support vector machine (SVM). Results show that the proposed system which needs no training procedure performswell in the ultrasonic classification of breast tumors with thehighest accuracy of 94.7%.

关键词： Breast tumors Cancer Artificial neural networks Ultrasonic imaging Support vector machines Support vector machine classification classification algorithms Clustering algorithms Feature extraction Breast neoplasms

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Optimizing Academic Conference classification Using Social Tags

Optimizing Academic Conference Classification Using Social T...

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Jing Xia Kunmei Wen Ruixuan Li Xiwu Gu Huazhong University of Science and Technology Wuhan China

Automatically classifying academic conference into semantic topic promises improved academic search and browsing for users. Social tagging is an increasingly popular way of describing the topic of academic conference. However, no attention has been devoted to academic conference classification by making use of social tags. Motivated by this observation, this paper proposes a method which utilizes social tags as well as the content of academic conference in order to improve automatically identifying academic conference classification. The proposed method applies different automatic classification algorithms to improve classification quality by using social tags. Experimental results show that this method mentioned above performs better than the method which only utilizes the content to classify academic conference with 1% Precision measure score increase and 1.64% F1 measure score increase, which demonstrates the effectiveness of the proposed method.

关键词： classification algorithms Vocabulary Support vector machine classification Training Web pages Prediction algorithms Testing

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Tactic classification of broadcast soccer videos by using AI

Tactic classification of broadcast soccer videos by using AI

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IEEE Integrated STEM Education Conference (ISEC)

作者： Jioh In Princeton International School of Mathematics and Science USA

ISBN: (纸本)9781665484305

Modern soccer games are exciting to watch not only for individual techniques of stylish players but also for managers’ sharp and creative tactics. As if playing chess, managers set the number of strikers and defenders and manage the game through various formations and flexible tactical changes. Since tactics have become a key factor in winning, soccer clubs hire high-quality managers for tactical knowledge and data scientists for better data analysis. Sports data scientists analyze previous game videos to identify past games’ problems and analyze opponents’ game styles. By combining scientists’ data and the data of the players collected through the wearable tracker, the manager predicts the opponent’s tactic and prepares the tactic of the next game. However, data scientists’ game video analysis has problems. Data scientists directly review the game and add labels to the video hand by hand, which is a time-consuming task and makes it impossible to analyze the game in real-time. In addition, wearable trackers are expensive and heavy, which can be a price burden for amateur clubs and affect players’ performance. This research aims to extract tactics from broadcasting soccer game videos automatically. Since it analyzes soccer broadcast videos, I expect that not only coaches and data scientists but also ordinary soccer fans can easily analyze tactics. There are two ways to do this research. The first method is detecting the players and the ball in the video with convolutional neural network (CNN)-based object detection. Teams and player names are assigned to each player after object detection. The objects are tracked by using the deepSORT algorithm. The detected players and ball location information and interactions such as passes, shots, and tackles between players and players or between players and ball are quantified in each game. Using the quantified information from videos as inputs, the tactics are finally classified with supervised machine learning algorithms. The

关键词： Machine learning algorithms Games Object detection Broadcasting Real-time systems classification algorithms Multimedia communication

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A Novel classification Method for Motor Imagery Based on Brain-Computer Interface

A Novel Classification Method for Motor Imagery Based on Bra...

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International Joint Conference on Neural Networks

作者： Chih-Yu Chen Chun-Wei Wu Chin-Teng Lin Shi-An Chen Brain Research Center National Chiao Tung University Hsinchu Taiwan Depart. of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan

Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.

关键词： Electroencephalography Feature extraction Accuracy Brain modeling Covariance matrices classification algorithms Brain-computer interfaces

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Sports Image classification and Application based on SIFT Algorithm

Sports Image Classification and Application based on SIFT Al...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Bin Yang Beijing Youth Politics College Beijing China

ISBN: (纸本)9781665493970

The research on moving image classification and application based on SIFT algorithm is an important research work. This is the first time to conduct this type of research in the field of computer vision. SIFT algorithm is a method for finding key points in an image. It works by finding edges that are similar to each other and then identifying the relationship between them. The more similar two images are, the closer they are on the image. The main purpose of this study is to develop a model that can classify moving images with high accuracy and low computational cost. This method uses SIFT feature descriptor to divide sports images into four categories: football, basketball, volleyball and football. This method has been tested with other standard methods.

关键词： Computer vision Image color analysis Image edge detection Computational modeling Feature extraction classification algorithms Computational efficiency

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Development of classification System for Corporate Portal User Requests

Development of Classification System for Corporate Portal Us...

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International Conference on Information Technologies in Engineering Education (Inforino)

作者： Eduard Chelyshev Alexander Bobryakov Marina Raskatova Dmitry Shcherbakov Nadezhda Naryadkina Dept. of Computing Machines Systems and Networks National Research University "MPEI" Moscow Russia Dept. of Control and Information Technology National Research University "MPEI" Moscow Russia Smolensk branch National Research University "MPEI" Smolensk Russia Dept. of Industrial Thermal Power Systems National Research University "MPEI" Moscow Russia

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The paper is devoted to the development of classification system for corporate portal user requests using machine learning algorithms. Text corpus preprocessing including irrelevant characters removal, normalization and stop words exception was performed. Two different feature generation methods were tried for vector representation building of words: the TF-IDF method and the FastText vector space model. Data set was split into training, validation and test samples. Experimental search for the optimal value of the feature space dimension using the TF-IDF method was carried out. Four classification algorithms were applied to solve the task: logistic regression, Gaussian naïve Bayesian classifier, k-nearest neighbors classifier and artificial neural network. The classifiers were trained on the training sample. The grid search algorithm was used for the most appropriate hyperparameter detection. The developed system performance was tested using several classification metrics on the validation and test samples.

关键词： Measurement Training Logistic regression System performance Artificial neural networks Vectors classification algorithms

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Music Vocal Spectrum classification Model based on Improved SVM Algorithm

Music Vocal Spectrum Classification Model based on Improved ...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Mengyi Xiang Zhaoqing University Zhaoqing China

ISBN: (纸本)9781665489638

Music vocal spectrum classification model based on improved SVM algorithm is studied. There are two main types of the current music classification and detection models in complex noise environmentsn manely the linear and non-linear methods. Through the in-depth analysis on comparing the differences, this paper select the improved SVM as the tool. We firstly use the preprocessing model to normalize the collected vocal signal to serve as the basic step for the later information processing; Then, we conduct the music signal characteristic analysis to be the basis for the feature selection; Lastly, we design the novel improved SVM algorithm to classify the signal. After simulating the proposed model with MATLAB, the performance is visualized.

关键词： Support vector machines Visualization Analytical models Process control Rhythm Mathematical models classification algorithms

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