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Decomposition of Generalized Asymmetry Model for Square Contingency Tables

Open Journal of Statistics 2016年第3期6卷 405-411页

作者： Shuji Ando Hiroyuki Kurakami Data Management & Biostatistics Group Re-Examination Department Novartis Pharma K.K. Tokyo Japan Department of Information Sciences Faculty of Science and Technology Tokyo University of Science Chiba Japan

For the analysis of square contingency tables with same row and column ordinal classifications, the present paper gives the decomposition of the generalized linear diagonals-parameter symmetry model using the diagonals-parameter symmetry model. Moreover, it gives the decomposition of the symmetry model using above the proposed decomposition.

关键词： Diagonals-Parameter Symmetry Linear Diagonals-Parameter Symmetry Orthogonality Symmetry

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

arXiv

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arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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Arabic Sentiment Analysis with Optimal Combination of Features Selection and Machine Learning Approaches

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Research Journal of Applied sciences, Engineering and technology 2016年第5期13卷 386-393页

作者： Bilal Sabri Saidah Saad Data Mining and Optimization Research Group (DMO) Centre for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technologi School of Computer Science Universiti Kebangsaan Malaysia (UKM) 43600 Bandar Baru Bangi Malaysia

The main objective of this research study is to design a model that allows for the utilization of a novel technique for the implementation of sentiment analysis in the Arabic language. Sentiment analysis is an interesting task that includes web mining, Natural Language Processing (NLP) and Machine Learning (ML). Most of the research work on sentiment analysis was focused on the texts in the English language. Therefore, the research on sentiment analysis in the Arabic language and other languages are in the infancy stage. This study empirically evaluates three Feature Selection Methods (FSM) (information Gain (IG), Chi-square (CHI) and Gini Index (GI)) and, three classification approaches (Association Rule (AR) mining and the N-gram model and the Meta-classifier approach) for the implementation of sentiment classification in the Arabic language. A number of related experiments have been carried out on the Opinion Corpus of Arabic (OCA). The results obtained from the experiments were favorable, depending on the algorithms used and the number of selected feature has proven that the use of FS method can increase the performance of sentiment classification in the Arabic language. The results of the experiments reveal that FS method is obtained to develop the classifier performance. Furthermore, the results of the experiment indicated that the use of CHI feature selection has produced the best performance for FS and the performance of meta-classifier a combination approach has outperformed the other approaches for sentiment classification in the Arabic language. In conclusion, this research study has proven that the combination approach (meta-classifier) with the chi-square FS method produces the most accurate classification technique, as high as 90.80%.

关键词： Feature selection meta-classifier machine learning approach NLP opinion mining sentiment analysis

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Computational analysis of the structural properties of economic and financial networks

arXiv

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arXiv 2017年

作者： Emmert-Streib, Frank Musa, Aliyu Baltakys, Kestutis Kanniainen, Juho Tripathi, Shailesh Yli-Harja, Olli Jodlbauer, Herbert Dehmer, Matthias Predictive Medicine and Data Analytics Lab Department of Signal Processing Tampere University of Technology Finland Institute of Biosciences and Medical Technology Tampere Finland Industrial and Information Management Tampere University of Technology Tampere Finland Institute for Systems Biology Seattle United States Institute for Intelligent Production Faculty for Management University of Applied Sciences Upper Austria Steyr Campus Austria Department for Biomedical Computer Science and Mechatronics UMIT - The Health and Lifesciences University Hall in Tyrol Austria College of Computer and Control Engineering Nankai University Tianjin300350 China

In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems. In this paper, we are surveying the use of networks and network-based methods for studying economy related questions. We start with a brief overview of graph theory and basic definitions. Then we discuss descriptive network measures and network complexity measures for quantifying structural properties of economic networks. Finally, we discuss different network and tree structures as relevant for applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Finance

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Random analysis of statistical rough set

Random analysis of statistical rough set

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Eric C. C. Tsang Su-Yun Zhao Faculty of Information Technology Macau University of Science and Technology Macau China MOE Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) Beijing China

ISBN: (纸本)9781509003914

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling.

关键词： Rough sets Standards Cybernetics Fluctuations Stability analysis Big data Algorithm design and analysis

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Topic modeling for social media content: A practical approach

Topic modeling for social media content: A practical approac...

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International Conference on Computer and information sciences (ICCOINS)

作者： Vala Ali Rohani Shahid Shayaa Ghazaleh Babanejaddehaki Department of Data Analytics Berkshire Media Kuala Lumpur Malaysia Faculty of Computer Science and Information Technology Univerity Purtra Malaysia Kuala Lumpur Malaysia

Perceiving the discussed topic in social media brings a great amount of value to different fields, such as marketing, security, education, and management. Topic modeling provides a powerful method for projecting text documents into topic space. In this paper, we explore an unsupervised topic modeling approach that incorporates LDA algorithm toward discovering the topics in social media content. Empirical experiments on social media datasets with 90,527 records reveal that this approach is quite effective for detecting the topic facets and extracting their dynamics over time. By analyzing the studied dataset, five main topics were discovered accurately by the presented algorithm according to the domain experts' comments. The presented model is quite general and can be applied in a wide variety of domains to automatically mining topics from the social media channels.

关键词： Social network services Computational modeling Probabilistic logic data models Resource management Hidden Markov models Computers

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Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

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AIP Conference Proceedings 2017年第1期1891卷

作者： Siti Rohaidah Ahmad Nurhafizah Moziyana Mohd Yusop Azuraliza Abu Bakar Mohd Ridzwan Yaakub 1Department of Computer Science Faculty of Defence Science & Technology Universiti Pertahanan Nasional Malaysia 57000 Kuala Lumpur Malaysia 2Data Mining and Optimization Research Group Centre for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 46000 Bangi Selangor Malaysia

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

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Promoting Business Analytics and Quantitative Management of technology: Preface for ITQM 2016

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Procedia Computer science 2016年 91卷 1-8页

作者： Lee, Heeseok Shi, Yong Lee, Jongwon Cordova, Felisa Dzitac, Ioan Kou, Gang Li, Jaiping College of Business Korea Advanced Institute Science and Technology Dongdaemum-gu Seoul130-722 Korea Republic of Research Center on Fictitiotus Economy and Data Science Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Business Administration Hoseo University Asan-si Chungcheongnam-do336-795 Korea Republic of Faculty of Engineering and Business University Finis Terrae Pedro de Valdivia 1509. Providencia Santiago Chile Universitatea Agora Agora University Piata Tineretului 8 Oradea410526 Romania School of Business Administration Southwestern University of Finance and Economics Chengdu Sichuan61130 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China

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Remote Sensing and Land Suitability Analysis to Establish Local Specific Inputs for Paddy Fields in Subang, West Java

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Procedia Environmental sciences 2016年 33卷 94-107页

作者： Widiatmaka Wiwin Ambarwulan Paulus B.K. Santoso Supiandi Sabiham Machfud Muhammad Hikmat Department of Soil Science and Land Resources Bogor Agricultural University Bogor 16680 Indonesia Geospatial Information Agency Indonesia Center for Data and Information Ministry of Agriculture Indonesia Faculty of Agricultural Technology Bogor Agricultural University Indonesia Soil Research Institute Ministry of Agriculture Indonesia

Indonesia requires a considerable increase in rice production in the future to keep pace with rapid population growth. Increased production can be done through the specific location-based inputs provision. This study aimed to map land resources in Subang Regency, West Java Province, at a scale of 1:25,000, to evaluate land suitability on any land mapping units and to recommend local specific inputs for paddy fields. The methodology used included land mapping unit delineation, delineation of existing paddy fields using SPOT-6 imagery, soil sampling and laboratory analysis, and land suitability evaluation for the paddy fields. The results showed that in Subang Regency, most paddy fields still allow for intensification through the provision of inputs that are location specific. In the case of the Subang Regency, the local specific inputs include provision of organic matter, provision of lime, provision of N, P and K fertilizer, and construction of irrigation infrastructure. Using the Geographic information System, the location where the input is necessary can be given in detail. Moreover, beyond the existing paddy field, there are still areas where actual land use is not paddy field but are identified as having potential for the development of paddy fields. As in the case of existing paddy fields, such areas require local specific inputs according to land characteristics that differ from one place to another and can be spatially delineated.

关键词： temporal automated land evaluation system land characteristic land use and land cover SPOT-6 imagery

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