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2nd International Conference on Simulation and Modeling Methodologies,Technologies and Applications(SMTA 2015)

作者： Yong-xin Qin Rong Hu Zhao-min Ma Bo Hu Yu-qiu Shi College of Electronic & Information Engineering Guangxi University of Science and Technology

Floating bottles is one of the most common pollutant of our water. As automatic monitoring is the basis of prompt and effective waste management, a new method of automatic machine image monitoring is proposed. The original image on the surface of the water is firstly segmented, then the segmented image is processed with 5 morphological operations including dilation, closing, skeleton, skeleton after dilation and skeleton after closing. Then 6 areas characteristics are extracted from the segmented images and the 5 morphological images. Finally, the need for the salvage of the floating bottles is identified with the 6 areas characteristics based on the KNN algorithm. There are 80 images used as training samples and 50 images used as test samples in the experiments. The results of the experiments show that compared with manual identification, the accuracy rates of the training samples and test samples are always 90% higher with different KNN algorithm parameters. This means that the method is effective for the automatic monitoring of floating bottles on the water surfaces and should be helpful for the automatic salvage of waste floating bottles.

关键词： Floating bottle machine vision K Nearest Neighbour(KNN) classification algorithm

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A Hybrid Monitoring Method for Robust Multivariate Process Control

A Hybrid Monitoring Method for Robust Multivariate Process C...

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SCIEI 2015 Singapore Conference

作者： Sung Ho Park Seoung Bum Kim Korea University/ Department of Industrial Management Engineering

Control chart,the most representative statistical process control technique,can be considered to share a common goal with classification algorithms in that both of them aim at estimating the decision boundaries that classify the observations into either in-control or the out-of-control *** main limitation that conventional control chart cannot take advantage of using out-of-control information can be eased by integrating classification algorithms with existing monitoring *** the present study,we proposed a hybrid scheme that combines T2 control chart and binary class classification algorithms by eliciting the nature of class *** and real case studies demonstrated that the proposed method can be effectively used for monitoring multivariate processes when their distributions are nonnormally distributed or unknown.

关键词： Multivariate control chart classification algorithm Hybrid monitoring statistic

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Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer

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Translational Proteomics 2014年第1期2卷 52-59页

作者： Sonntag, Johanna Bender, Christian Soons, Zita der Heyde, Silvia von König, Rainer Wiemann, Stefan Sinn, Hans-Peter Schneeweiss, Andreas Beißbarth, Tim Korf, Ulrike Division of Molecular Genome Analysis German Cancer Research Center (DKFZ) Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Network Modeling Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute Jena Jena Germany Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Integrated Research and Treatment Center Center for Sepsis Control and Care (CSCC) Jena University Hospital Jena Germany Department of Pathology University of Heidelberg Heidelberg Germany National Center for Tumor Diseases (NCT) University of Heidelberg Heidelberg Germany TRON - Translational Oncology Johannes Gutenberg University of Mainz Medicine Mainz Germany Department of Knowledge Engineering Maastricht University Maastricht Netherlands

A robust subclassification of luminal breast cancer, the most common molecular subtype of human breast cancer, is crucial for therapy decisions. While a part of patients is at higher risk of recurrence and requires chemo-endocrine treatment, the other part is at lower risk and also poorly responds to chemotherapeutic regimens. To approximate the risk of cancer recurrence, clinical guidelines recommend determining histologic grading and abundance of a cell proliferation marker in tumor specimens. However, this approach assigns an intermediate risk to a substantial number of patients and in addition suffers from a high interobserver variability. Therefore, the aim of our study was to identify a quantitative protein biomarker signature to facilitate risk classification. Reverse phase protein arrays (RPPA) were used to obtain quantitative expression data for 128 breast cancer relevant proteins in a set of hormone receptor-positive tumors (n = 109). Proteomic data for the subset of histologic G1 (n = 14) and G3 (n = 22) samples were used for biomarker discovery serving as surrogates of low and high recurrence risk, respectively. A novel biomarker selection workflow based on combining three different classification methods identified caveolin-1, NDKA, RPS6, and Ki-67 as top candidates. NDKA, RPS6, and Ki-67 were expressed at elevated levels in high risk tumors whereas caveolin-1 was observed as downregulated. The identified biomarker signature was subsequently analyzed using an independent test set (AUC = 0.78). Further evaluation of the identified biomarker panel by Western blot and mRNA profiling confirmed the proteomic signature obtained by RPPA. In conclusion, the biomarker signature introduced supports RPPA as a tool for cancer biomarker discovery. © 2014 The Authors.

关键词： Biomarker classification algorithm Luminal breast cancer Reverse phase protein array Risk classification

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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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Medical Document classification Based on MeSH

Medical Document Classification Based on MeSH

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47th Annual Hawaii International Conference on System Sciences

作者： Alghoson, Abdullah Muhammad Claremont Grad Univ Claremont CA 91711 USA

ISBN: (纸本)9781479925049

One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. I am developing a classification algorithm that classifies a medical document by analyzing its content and categorizing it under predefined topics from the Medical Subject Headings (MeSH). I collected a corpus of 50 full-text journal articles (N=50) from MEDLINE, which were already indexed by experts based on MeSH. Using natural language processing (NLP), my algorithm classifies the collected articles under MeSH subject headings. I evaluated the algorithm's outcome by measuring its precision and recall of resulting subject headings from the algorithm, comparing results to the actual documents' subject headings. The algorithm classified the articles correctly under 45% to 60% of the actual subject headings and got 40% to 53% of the total subject headings correct. This holds promising solutions for the global health arena to index and classify medical documents expeditiously.

关键词： classification medical information systems natural language processing text analysis MEDLINE MeSH subject heading NLP classification algorithm collected articles document subject heading full-text journal articles global health arena information systems medical document classification medical documents medical subject heading natural language processing unstructured text classification algorithms Indexing Libraries Measurement Medical services Natural language processing MeSH Medical Document classification based on MeSH using MeSH

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Feature selection and classifier performance on diverse bio- logical datasets

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BMC BIOINFORMATICS 2014年第13期15卷 1-14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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