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IEEE International Conference on Systems, Man, and Cybernetics

作者： Xiaolong Zhang Chao Cheng School of Computer Science and Technology Wuhan University of Science and Technology

ISBN: (纸本)9781467317139

Traditional classification algorithms are difficult in dealing with imbalance data. This paper proposes a classification algorithm called CascadeBoost, which combines with the advantages of boosting algorithm and cascade model that can learn imbalance data. Cascade model allows the pre-training data to be balanced by gradually reducing the number of the major class;and then the most rich information samples based on the weight distribution can be gradually selected using boosting algorithm. The experimental results show that the proposed method obtains better performance compared to other methods.

关键词： Imbalance Data Boosting algorithm Cascade Model SVM AUC Boost sorting algorithm cascades data classification

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CLASSIFYING THE CANADIAN BOREAL FOREST'S STRUCTURE USING MULTI-MODAL REMOTE SENSING

CLASSIFYING THE CANADIAN BOREAL FOREST'S STRUCTURE USING MUL...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Michael L. Benson Leland E. Pierce Kathleen M. Bergen Kamal Sarabandi The University of Michigan The Radiation Laboratory EECS Dept. The University of Michigan School of Natural Resources and the Environment

ISBN: (纸本)9781467311601

One of the most fundamental new technical challenges of a DESDynI-R space-borne mission is the fusion of the several sensor modalities, including the onboard SAR and external LiDAR and Optical sensors in order to accurately estimate desired 3D vegetation structures and biomass parameters in areas where the sensors overlap. The objective of this paper is to use measured datasets in conjunction with our sensor models to develop a classification algorithm that fuses multi-modal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height.

关键词： SAR LiDAR Landsat Forest structure Classification Fractal tree VIR Fusion BOREAS DESDynI sorting algorithm Sensors remote sensing optical sensors Forest forest structure Fusion reactions FUSION fusion (melting) Remote Sensing Technologies

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Comparison of Detection and Classification algorithms Using Boolean and Fuzzy Techniques

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ADVANCES IN FUZZY SYSTEMS 2012年第unknown期2012卷 406204.1-406204.10页

作者： Dixit, Rahul Singh, Harpreet Wayne State Univ Dept Engn & Comp Engn Detroit MI 48202 USA Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

Modern military ranging, tracking, and classification systems are capable of generating large quantities of data. Conventional "brute-force" computational techniques, even with Moore's law for processors, present a prohibitive computational challenge, and often, the system either fails to "lock onto" a target of interest within the available duty cycle, or the data stream is simply discarded because the system runs out of processing power or time. In searching for high-fidelity convergence, researchers have experimented with various reduction techniques, often using logic diagrams to make inferences from related signal data. Conventional Boolean and fuzzy logic systems generate a very large number of rules, which often are difficult to handle due to limitations in the processors. Published research has shown that reasonable approximations of the target are preferred over incomplete computations. This paper gives a figure of merit for comparing various logic analysis methods and presents results for a hypothetical target classification scenario. Novel multiquantization Boolean approaches also reduce the complexity of these multivariate analyses, making it possible to better use the available data to approximate target classification. This paper shows how such preprocessing can reasonably preserve result confidence and compares the results between Boolean, multi-quantization Boolean, and fuzzy techniques.

关键词： Fuzzy multivariate analysis Quality factor PROCESSOR sorting algorithm Taxonomy processing power Targets logic diagram LARGE QUANTITIES data flow fuzzy logic system DUTY CYCLE

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Unsupervised Hyperspectral Image Classification algorithm By Integrating Spatial-Spectral Information

Unsupervised Hyperspectral Image Classification Algorithm By...

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International Conference on Audio, Language and Image Processing

作者： Belkacem Baassou Mingyi He Shaohui Mei Yifan Zhang Shaanxi Provincial Key Laboratory of Information Acquisition and Processing (IAP) School of Electronics and Information Northwestern Polytechnical University Shaanxi Provincial Key Laboratory of Information Acquisition and Processing (IAP)School of Electronics and Information Northwestern Polytechnical University

ISBN: (纸本)9781467301732

An integrated spatial-spectral information algorithm for hyper spectral image classification is proposed, which uses spatial pixel association (SPA)by exploiting spectral information divergence (SID) and spectral clustering to reduce regions number and improve classification accuracy. Moreover, a class boundary correction method is also developed to minimize the misclassified pixels at the edge of each class and to solve the problem of merged classes. Experiments with hyper spectral data demonstrate the effectiveness and advantages of the proposed frame work over some traditional methods in term of classification accuracy.

关键词： frame work Image classification hyperspectral imagery sorting algorithm pixel TRADITIONAL METHODS Sudden Infant Death

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FEATURE SELECTION BY HIGH DIMENSIONAL MODEL REPRESENTATION AND ITS APPLICATION TO REMOTE SENSING

FEATURE SELECTION BY HIGH DIMENSIONAL MODEL REPRESENTATION A...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Gulsen Taskin Kaya Huseyin Kaya Okan K. Ersoy Istanbul Technical Univ. Informatics Institute Purdue Univ. School of Electrical and Computer Eng.

ISBN: (纸本)9781467311601

In supervised classification, performance of a classification algorithm depends on several factors when dealing with a data set with a large number of features. One of them is the size of training data used in classification with respect to the number of features. Having less number of training data than the number of features can cause a reduction in classification performance with the addition of more features, called Hughes effect. A large number of features does not also guarantee higher classification accuracy as some of these features may be irrelevant for predicting the class labels. Additionally, the more features used during the training process, the higher is the computational time elapsed. Therefore, in terms of classification accuracy and less computational time, dimensionality reduction in feature size should be considered during classification when having the data with a large number of features. Two categories for reduction of dimensionality have been commonly encountered in the literature;feature extraction and feature selection. In feature extraction methods, new features are generated by taking linear combination of existing features. Fisher's linear discriminant analysis (LDA) is one of the most commonly used methods which aim to estimate a linear transformation to map data from a high dimensional space into a low-dimensional subspace that maintains unchanged separability. There are also many feature extraction algorithms developed and applied to hyperspectral images in the literature. Benediktsson et al. used decision boundary feature extraction method (DBFE) in classification of hyperspectral data with neural networks [1]. Discriminant analysis feature extraction (DAFE) was also another feature extraction method used for dimensionality reduction in classification. Kuo et al. proposed a new nonparametric feature extraction method (NWFE) based on scatter matrices for reduction of features of high-dimensional data[2].

关键词： Feature extraction dimensionality reduction remote sensing sorting algorithm computational time Linear Combination linear transformations

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A Wearable Activity Sensor System and Its Physical Activity Classification Scheme

A Wearable Activity Sensor System and Its Physical Activity ...

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International Joint Conference on Neural Networks

作者： Fang-Chen Chuang Jeen-Shing Wang Ya-Ting Yang Tzu-Ping Kao Department of Electrical Engineering National Cheng Kung University Institute of Education National Cheng Kung University

ISBN: (纸本)9781467314886

This paper presents a wearable activity sensor system and a systematic activity classification scheme for the classification of human daily physical activities. The wearable activity sensor system, consisting of two activity sensor modules worn on users' dominant hand wrists and ankles, is used for collecting activity acceleration signals. The proposed activity classification scheme, including static/dynamic activity analysis, posture recognition, exercise classification, and ambulation classification, is capable of classifying time-series activity acceleration signals. The collected acceleration signals are classify into two categories by means of static/dynamic activity analysis. Posture recognition is applied for partitioning static signals into sitting and standing. Exercise classification and ambulation classification algorithms were used to classify dynamic activity signals. Our experimental results have successfully validated the effectiveness of the proposed wearable sensor system and the scheme of activity classification algorithms with an overall classification accuracy of 96% for seven types of daily activities.

关键词： Physical activity classification Posture recognition Accelerometer K-nearest neighbor classification classification scheme Sensor systems Wearable Ankle Daily Activities Sitting Position signals sorting algorithm activity analysis Motor Activity

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SAR image classification based on alpha-stable distribution

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REMOTE SENSING LETTERS 2011年第1期2卷 51-59页

作者： Peng, Yijin Xu, Xin Zhou, Wanbin Zhao, Yin Wuhan Univ Signal Proc Lab Sch Elect Informat Wuhan 430079 Hubei Peoples R China

We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multi-level SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.

关键词： sorting algorithm Synthetic aperture radar Support Vector Network Eigenvector Image classification images algorithms Imagery (Psychotherapy) Rescue SAFETY ANALYSIS REPORTS distributed parameter Specific absorption rate

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A comparison of classification algorithms for the identification of smoke plumes from satellite images

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STATISTICAL METHODS IN MEDICAL RESEARCH 2011年第2期20卷 131-156页

作者： Wan, V. Braun, W. J. Dean, C. B. Henderson, S. Simon Fraser Univ Dept Stat & Actuarial Sci Burnaby BC V5A 1S6 Canada Univ Western Ontario Dept Stat & Actuarial Sci London ON N6A 5B7 Canada Univ British Columbia Sch Environm Hlth Vancouver BC V5Z 1M9 Canada

Obtaining accurate measures of exposure to forest fire smoke is important for the assessment of health risk. Estimating exposure from air quality monitors is challenging because of the sparseness of the monitoring networks in remote areas. However, satellite imagery offers a novel and data-rich tool to provide visual information on smoke plumes. We will discuss statistical techniques for obtaining estimates of forest fire smoke plumes using classification algorithms on data from satellite imagery in order to develop automated processes for identifying exposure. The aim is to identify whether such methods may offer a high-resolution approach that provides a reliable estimate of smoke and a more thorough caption of the spatial distribution of smoke from fires than is currently available.

关键词： Health risk assessment Smoke visual information statistical techniques sorting algorithm

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A Power Wheelchair Controlled using Hand Gestures, a Single sEMG Sensor, and Guided Under-determined Source Signal Separation

A Power Wheelchair Controlled using Hand Gestures, a Single ...

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IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Luis A. Rivera Guilherme N. DeSouza Department of Electrical and Computer Engineering University of Missouri

ISBN: (纸本)9781457711992

Surface Electromyographic signals (sEMG) find applications in many areas such as rehabilitation, prosthesis and human-machine interaction. Systems reliant on these muscle-generated electrical signals require some form of machine learning algorithm for recognition of specific patterns of muscle activity. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used, however, in all those cases, the use of multiple sensors is a constant requirement. In this paper, we present a power wheelchair control system that relies on a single sEMG sensor and a new technique for signature recognition called Guided Under-determined Source Signal Separation (GUSSS). Compared to other approaches in the literature, the proposed technique achieves comparable results even when using a simple distance classifier and a very small number of features.

关键词： Electric wheelchair SEMA5B gene Source separation sorting algorithm Machine learning algorithms Sensors Gestures Recognition (Psychology) Feature extraction

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Classification Based on Distribution of Average Matching Degree and Gaussian Function and Its Application to Intrusion Detection

Classification Based on Distribution of Average Matching Deg...

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SICE Annual Conference

作者： Yuhong Li Shingo Mabu Nannan Lu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9781467322591

With the rapid development of the Internet, Internet security is becoming an important problem recently. Therefore, many techniques for intrusion detection have been proposed to protect networks effectively. In this paper, a new classification model, named classification with average matching degree and gaussian function, is proposed and combined with the class association rule mining of Genetic Network Programming (GNP). The proposed classification algorithm can efficiently classify a new access data into a class of normal, misuse or anomaly. The simulations are based on NSL-KDD data set.

关键词： Data Security Gauss equation sorting algorithm Taxonomy Network security

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