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2015 International Industrial Informatics and Computer Engineering Conference(IIICEC 2015)

作者： Zhao Ming Bohai University

Support vector machine is a classification algorithm emerged in recent years and has been successfully applied to many areas,and least squares support vector machine is a technology developed from the traditional support vector machine and has important researching ***,this paper introduces the basic idea of SVM and algorithms;secondly to study the basic principles of least squares support vector machine,concrete algorithm description,including the kernel function,etc;finally,this paper studied the application of the algorithm in the classification,the least squares support vector machine as a novel artificial intelligence technology is an extension of the standard support vector machine and has been more widely used in various disciplines,with global optimization,good marketing ability and other features,so this research has some theoretical significance.

关键词： classification algorithm SVM least square support vector machine kernel function

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Sparse classification of Hyperspectral Image Based on First-Order Neighborhood System Weighted Constraint

Sparse Classification of Hyperspectral Image Based on First-...

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Conference on Satellite Data Compression, Communications, and Processing X

作者： Liu, Jiahui Guan, Hui Li, Jiaojiao Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Beijing Inst Spacecraft Syst Engn Beijing 100094 Peoples R China

ISBN: (纸本)9781628410617

In hyperspectral image classification, each hyperspectral pixel can be represented by linear combination of a few training samples in the training dictionary. Assuming the training dictionary is available, the hyperspectral pixel can be recovered using a minimal training samples by solving a sparse representation problem, then the weighted coefficients of training samples are obtained and the class of the pixel can be determined, the above process is called classification algorithm based on sparse representation. However, the traditional sparse classification algorithms have not fully utilized the spatial information and classification accuracy is relatively low. In this paper, in order to improve classification accuracy, a new sparse classification algorithm based on First-Order Neighborhood System Weighted (FONSW) constraint is proposed. Compared with other sparse classification algorithms, the experimental results show that the proposed algorithm has a smoother classification map and higher classification accuracy.

关键词： Hyperspectral image classification algorithm sparse representation First-Order Neighborhood System Weighted (FONSW) spatial information

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Feature selection and classifier performance on diverse bio- logical datasets

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BMC BIOINFORMATICS 2014年第13-Sup期15卷 S4-S4页

作者： Hemphill, Edward Lindsay, James Lee, Chih Mandoiu, Ion I. Nelson, Craig E. Dept Mol & Cellular Biol Storrs CT 06269 USA Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA

Background: There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification algorithms to use with that biomarker set, can be a daunting task. Existing surveys of feature selection and classification algorithms typically focus on a single data type, such as gene expression microarrays, and rarely explore the model's performance across multiple biological data types. Results: This paper presents the results of a large scale empirical study whereby a large number of popular feature selection and classification algorithms are used to identify the tissue of origin for the NCI-60 cancer cell lines. A computational pipeline was implemented to maximize predictive accuracy of all models at all parameters on five different data types available for the NCI-60 cell lines. A validation experiment was conducted using external data in order to demonstrate robustness. Conclusions: As expected, the data type and number of biomarkers have a significant effect on the performance of the predictive models. Although no model or data type uniformly outperforms the others across the entire range of tested numbers of markers, several clear trends are visible. At low numbers of biomarkers gene and protein expression data types are able to differentiate between cancer cell lines significantly better than the other three data types, namely SNP, array comparative genome hybridization (aCGH), and microRNA data. Interestingly, as the number of selected biomarkers increases best performing classifiers based on SNP data match or slightly outperform those based on gene and protein expression, while those based on aCGH and microRNA data continue to perform the worst. It is observed that one class of feature selection and classifier are consistently top performers across data types and

关键词： Feature Selection Data Type Random Forest classification algorithm Feature Selection Method

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A New algorithm to Classify the Homogeneity of ERS-2 Wave Mode SAR Imagette

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2014年第1期42卷 13-21页

作者： Song, Guiting Panda, Jagabandhu Zhang, Yanhui Chen, Haoliang Krishna, K. Muni Zhejiang Ocean Univ Dept Marine Sci Zhoushan 316000 Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore 639798 Singapore Shandong Agr Univ Coll Resources & Environm Tai An Shandong Peoples R China Shandong Univ Key Lab Agr Environm Jinan 250100 Peoples R China Singapore MIT Alliance Res & Technol Singapore Singapore Andhra Univ Dept Meteorol & Oceanog Visakhapatnam Andhra Pradesh India

A new classification parameter is developed using 1535 ERS-2 wave mode synthetic aperture radar (SAR) test imagettes to better differentiate homogeneous and inhomogeneous imagettes. The comparison between the new parameter (Min) and the previous one (Inhomo) (Schulz-Stellenfleth and Lehner, 2004) was done under varied threshold values of Inhomo. It is concluded that the performance of 'Min' is much better than 'Inhomo' when applying to the 1535 test imagettes. Furthermore, both Min and Inhomo are applied to nearly 1 million imagettes collected for the period from 1 September 1998 to 30 November 2000. The comparisons of the global inhomogeneous distribution between 'Min' and 'Inhomo' reveal that both the areas and percentage of inhomogeneity calculated by 'Min' are larger than that calculated by 'Inhomo'. By analyzing the low wind speed distribution of HOAPS data, we found that low wind speed over the ocean is one of the key reasons for the inhomogeneity of SAR imagettes.

关键词： Synthetic aperture radar imagette Homogeneity classification algorithm

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A Kind of classification algorithms of Data Mining and Quantitative Analysis

A Kind of Classification Algorithms of Data Mining and Quant...

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3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012)

作者： Zhang, Yanfeng Li, Tingting Northwestern Polytech Univ Mech & Elect Sch Xian 710072 Peoples R China

ISBN: (纸本)9783037856482

C4.5, Bayesian network and Sequential Minimal Optimization (SMO) are three typical classification algorithms in data mining. Using cross-validation method with 10 folds get analysis and calculation results of the experiments for the three classification algorithms in the same training set and test set. The main metrics include accuracy, precision, speed, robustness, scalability and comprehensibility, we use margin curve show these. It provides a theoretical and experimental basis for users to select a proper classification algorithm with different training sets in quality and amount.

关键词： Data mining classification algorithm Training set Margin curve

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CRISP - An Interruption Management algorithm based on Collaborative Filtering 14

CRISP - An Interruption Management Algorithm based on Collab...

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32nd Annual ACM Conference on Human Factors in Computing Systems (CHI)

作者： Shrot, M. Tammar Rosenfeld, Avi Golbeck, Jennifer Kraus, Sarit Shamoon Coll Engn Dept Software Engn IL-84100 Beer Sheva Israel Jerusalem Coll Technol Dept Ind Engn IL-9116001 Jerusalem Israel Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Bar Ilan Univ Dept Comp Sci IL-5290002 Ramat Gan Israel

ISBN: (纸本)9781450324731

Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often require long training periods that make them unsuitable for online collaborative environments where new users frequently participate. In this paper, we present a novel synthesis between Collaborative Filtering methods and machine learning classification algorithms to create a fast learning algorithm, CRISP. CRISP exploits the similarities between users in order to apply data from known users to new users, therefore requiring less information on each person. Results from user studies indicate the algorithm significantly improves users' performances in completing the task and their perception of how long it took to complete each task.

关键词： Interruption Management (Cost Estimation) Collaborative Filtering classification algorithm

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Physiological Measurement used in Real Time Experiment to Detect Driver Cognitive Distraction 13

Physiological Measurement used in Real Time Experiment to De...

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13th International Conference on Electronics, Information, and Communication (ICEIC)

作者： Azman, Afizan Ibrahim, Siti Zainab Meng, Qinggang Edirisinghe, Eran A. Multimedia Univ Fac Informat Sci Technol Cyberjaya Selangor Malaysia Univ Loughborough Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781479937974

This paper discusses about lips and eyebrows are used to detect driver cognitive distraction by using faceAPI toolkit. A few number of classification algorithms like Support Vector Machine (SVM), Logistic Regression (LR) and Static Bayesian Network (SBN) and Dynamic Bayesian Network (DBN) have been used for accuracy rate comparison.

关键词： Lips & Eyebrows faceAPI FLM classification algorithm

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Data Dimension Reduction in Training Strategy for Face Recognition System

Data Dimension Reduction in Training Strategy for Face Recog...

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21st International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Loderer, Marek Pavlovicova, Jarmila Feder, Matej Oravec, Milos Slovak Univ Technol Bratislava Fac Elect Engn & Informat Technol Ilkovicova 3 Bratislava 81219 Slovakia

ISBN: (纸本)9789531841917

In this paper, we propose a training strategy for an automatic face recognition system. Our strategy is based on cascade reduction of data dimensionality using LBP and PCA algorithms. This method is able to achieve higher recognition accuracy in comparison with simple LBP or PCA and it is suitable in the case of adding a new user to the face recognition system. We provide a comparative study of our proposed algorithm and several standard algorithms that reduce dimensionality of input data. Dimension reduction is important also in the case of storage and computational complexity reduction. We propose an overview of selected strategies and we compare their performance using CMU PIE face database. Our results in testing of clustering algorithms indicate that SOM and K-means algorithms are suitable for an automatic selection of training samples for a recognition system. According to achieved results we propose a part of the face recognition system suitable for example for the next-generation of hybrid broadcast broadband television.

关键词： Face recognition Histogram equalization Feature extraction LBP LDA PCA Clustering algorithms SOM DBSCAN K-means classification algorithm SVM

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Human Activity Recognition in WSN: A Comparative Study

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INTERNATIONAL JOURNAL OF NETWORKED AND DISTRIBUTED COMPUTING 2014年第4期2卷 221-230页

作者： Awan, Muhammad Arshad Guangbin, Zheng Kim, Cheong-Ghil Kim, Shin-Dug Yonsei Univ Dept Comp Sci 134 Shinchon Dong Seoul 120749 South Korea Namseoul Univ Dept Comp Sci Cheonan South Korea

Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is still a burden on smartphones. Three major factors including;classification algorithm, data feature, and smartphone position influence the recognition accuracy and time. In this paper, we present a comparative study of six classification algorithms, six data features, and four different positions that are most commonly used in the recognition process using smartphone accelerometer. This analysis can be used to select any specific classification algorithm, data feature, and smartphone position for human activity recognition in terms of accuracy and response time. The methodology we used is composed of two major components;a data collector, and a classifier. A set of eleven activities of daily living, four different positions for data collection and ten volunteers contributed to make it a worth-full comparative study. Results show that K-Nearest Neighbor and J48 algorithms performed well both in terms of time and accuracy irrespective of data features whereas the performance of other algorithms is dependent on the selected data features. Similarly, mean and mode features gave good results in terms of accuracy irrespective of the classification algorithm. A short version of the paper has already been presented at ICIS 2014.

关键词： Activity recognition classification algorithm data feature smartphone position ubiquitous computing

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Traffic Pattern Recognition System Design and Development Based on Smart Phones 7

Traffic Pattern Recognition System Design and Development Ba...

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7th International Conference Intelligent Computation Technology Automation (ICICTA)

作者： Yu Ning Liu-xiaoxing Hebei Coll Sci & Technol Baoding 071000 Peoples R China

ISBN: (纸本)9781479966363

With the development of social intelligence and information, it is of more and more great significance in recognizing traffic patterns automatically. Traffic patterns recognition, refers to identifying the user's currently traffic pattern, which can be widely used in the transportation planning, location based services, social networks and many other fields. With the development and popularization of smart terminals, mobile phones have more and more powerful perception, computation, storage and communication abilities, making recognize user traffic patterns with the mobile phones a hot topic of current research. This paper firstly introduces the current research situation of the traffic patterns recognition. Then the positioning algorithms and classification algorithms are presented, and appropriate algorithms are selected for our systems. Based on the selected algorithms, the traffic patterns recognition system is developed with smart mobile phones, which has three main functions: sustaining traffic patterns recognition, record the current traffic information and view the history traffic locus.

关键词： Traffic Patterns positioning algorithm classification algorithm Smart Mobile Phone

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