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Scientific World Journal 2015年第1期2015卷 471371页

作者： Nguyen, Thanh-Tung Huang, Joshua Zhexue Nguyen, Thuy Thi Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Engineering Water Resources University Hanoi 10000 Viet Nam College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Faculty of Information Technology Vietnam National University of Agriculture Hanoi 10000 Viet Nam

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures. © 2015 Thanh-Tung Nguyen et al.

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Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

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Nature genetics 2018年第5期50卷 766-767页

作者： Valérie Turcot Yingchang Lu Heather M Highland Claudia Schurmann Anne E Justice Rebecca S Fine Jonathan P Bradfield Tõnu Esko Ayush Giri Mariaelisa Graff Xiuqing Guo Audrey E Hendricks Tugce Karaderi Adelheid Lempradl Adam E Locke Anubha Mahajan Eirini Marouli Suthesh Sivapalaratnam Kristin L Young Tamuno Alfred Mary F Feitosa Nicholas G D Masca Alisa K Manning Carolina Medina-Gomez Poorva Mudgal Maggie C Y Ng Alex P Reiner Sailaja Vedantam Sara M Willems Thomas W Winkler Gonçalo Abecasis Katja K Aben Dewan S Alam Sameer E Alharthi Matthew Allison Philippe Amouyel Folkert W Asselbergs Paul L Auer Beverley Balkau Lia E Bang Inês Barroso Lisa Bastarache Marianne Benn Sven Bergmann Lawrence F Bielak Matthias Blüher Michael Boehnke Heiner Boeing Eric Boerwinkle Carsten A Böger Jette Bork-Jensen Michiel L Bots Erwin P Bottinger Donald W Bowden Ivan Brandslund Gerome Breen Murray H Brilliant Linda Broer Marco Brumat Amber A Burt Adam S Butterworth Peter T Campbell Stefania Cappellani David J Carey Eulalia Catamo Mark J Caulfield John C Chambers Daniel I Chasman Yii-Der I Chen Rajiv Chowdhury Cramer Christensen Audrey Y Chu Massimiliano Cocca Francis S Collins James P Cook Janie Corley Jordi Corominas Galbany Amanda J Cox David S Crosslin Gabriel Cuellar-Partida Angela D'Eustacchio John Danesh Gail Davies Paul I W Bakker Mark C H Groot Renée Mutsert Ian J Deary George Dedoussis Ellen W Demerath Martin Heijer Anneke I Hollander Hester M Ruijter Joe G Dennis Josh C Denny Emanuele Di Angelantonio Fotios Drenos Mengmeng Du Marie-Pierre Dubé Alison M Dunning Douglas F Easton Todd L Edwards David Ellinghaus Patrick T Ellinor Paul Elliott Evangelos Evangelou Aliki-Eleni Farmaki I Sadaf Farooqi Jessica D Faul Sascha Fauser Shuang Feng Ele Ferrannini Jean Ferrieres Jose C Florez Ian Ford Myriam Fornage Oscar H Franco Andre Franke Paul W Franks Nele Friedrich Ruth Frikke-Schmidt Tessel E Galesloot Wei Gan Ilaria Gandin Paolo Gasparini Jane Gibson Vilmantas Giedraitis Anette P Gjesing Penny Gordon-Larsen Mathias Gorski Hans-Jörgen Montreal Heart Institute Université de Montréal Montreal Quebec Canada. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University School of Medicine Nashville TN USA. Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA. Genetics of Obesity and Related Metabolic Traits Program Icahn School of Medicine at Mount Sinai New York NY USA. Department of Epidemiology University of North Carolina Chapel Hill NC USA. Human Genetics Center University of Texas School of Public Health University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences University of Texas Health Science Center at Houston Houston TX USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genetics Harvard Medical School Boston MA USA. Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children's Hospital Boston MA USA. Center for Applied Genomics Division of Human Genetics Children's Hospital of Philadelphia Philadelphia PA USA. Quantinuum Research LLC San Diego CA USA. Estonian Genome Center University of Tartu Tartu Estonia. Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA. Institute for Translational Genomics and Population Sciences LABioMed at Harbor-UCLA Medical Center Torrance CA USA. Wellcome Trust Sanger Institute Hinxton UK. Department of Mathematical and Statistical Sciences University of Colorado Denver CO USA. Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK. Department of Biological Sciences Faculty of Arts and Sciences Eastern Mediterranean University Famagusta Cyprus. Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany. Department of Biostatistics and Center for Statistical Genetics University of M

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

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Big data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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Subsume concept in erasable itemset mining

Subsume concept in erasable itemset mining

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6th International Conference on Computational Collective Intelligence, ICCCI 2014

作者： Nguyen, Giang Le, Tuong Vo, Bay Le, Bac Trinh, Phi-Cuong University of Technology Ho Chi Minh City Viet Nam Division of Data Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology University of Science VNU Ho Chi Minh City Viet Nam Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9783319112886

In recent year, erasable itemset mining is an interesting problem in supply chain optimization problem. In the previous works, we presented dPidset structure, a very effective structure for mining erasable itemsets. The dPidset structure improves the preferment compared with the previous structures. However, the mining time is still large. Therefore, in this paper, we propose a new approach using the subsume concept for mining effectively erasable itemsets. The subsume concept helps early determine information of a large number of erasable itemsets without usual computational cost. The experiment was conducted to show the effectiveness of using subsume concept in the mining erasable itemsets process. © Springer International Publishing Switzerland 2014.

关键词： data mining

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A novel method for mining class association rules with itemset constraints

A novel method for mining class association rules with items...

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6th International Conference on Computational Collective Intelligence, ICCCI 2014

作者： Nguyen, Dang Vo, Bay Le, Bac Division of Data Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Computer Science Department University of Science VNU Ho Chi Minh Viet Nam

ISBN: (纸本)9783319112886

Mining class association rules with itemset constraints is very popular in mining medical datasets. For example, when classifying which populations are at high risk for the HIV infection, epidemiologists often concentrate on rules which include demographic information such as sex, age, and marital status in the rule antecedents. However, two existing methods, post-processing and pre-processing, require much time and effort. In this paper, we propose a lattice-based approach for efficiently mining class association rules with itemset constraints. We first build a lattice structure to store all frequent itemsets. We then use paternity relations among nodes to discover rules satisfying the constraint without re-building the lattice. The experimental results show that our proposed method outperforms other methods in the mining time. © Springer International Publishing Switzerland 2014.

关键词： data mining

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A New dataset and Benchmark for Cloud Computing Service Composition

A New Dataset and Benchmark for Cloud Computing Service Comp...

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International Conference on Intelligent Systems, Modelling and Simulation

作者： Amin Jula Hamid Nilsaz Elankovan Sundararajan Zalinda Othman Data Mining and Optimization Research Group Centre for Artificial Intelligence Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Department of Mathematics Mahshahr branch Islamic Azad University Data Mining and Optimization Research Group Centre for Artificial Intelligence Centre of Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia

ISBN: (纸本)9781479938599

Cloud computing as an effective computing approach is attracting increasingly the attention of heavy processing applicants to its capabilities. Cloud suppliers try to prevent any service-request unanswered utilizing a large number of service providers. Selecting optimal unique-services in cloud computing service composition (CCSC) is a noteworthy problem that must be addressed extremely accurate and scrupulously. Recently, considerable studies have been done for solving CCSC, however lack of widely accepted and reliable CCSC problems for fair and equitable comparison of proposing methods, is a blind spot and should be considered as a high-priority problem. In this paper, a reliable set of ten CCSC problems is introduced (CCSC_Benchmark) which is prepared to be used as a "standard test" in future works. To complete the usability of the problem set, the best solutions of the generated problems are also found and presented to provide a facility for calculating error rate of the proposed methods.

关键词： Cloud Computing Service Composition Service Selection Benchmark Best Practices Standard Test dataset Quality of Service Parameters QoS

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Sightseeing route planning problem by electric vehicle on the Time-Expanded Network 7

Sightseeing route planning problem by electric vehicle on th...

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2014 IEEE 7th International Workshop on Computational Intelligence and Applications, IWCIA 2014

作者： Hasuike, Takashi Tsubaki, Hiroe Katagiri, Hideki Tsuda, Hiroshi Graduate School of Information Science and Technology Osaka University 2-1 Yamadaoka SuitaOsaka565-0871 Japan Department of Data Science Institute of Statistical Mathematics 10-3 Midorimachi TachikawaTokyo190-8562 Japan Graduate School of Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-HiroshimaHiroshima739-8527 Japan Faculty of Science and Engineering Doshisha University 1-3 Tatara Miyakodani KyotanabeKyoto610-0321 Japan

ISBN: (纸本)9781479947706

This paper proposes a sightseeing route planning problem by electric vehicle with time-dependency of traveling times between any two sightseeing sites. In order to represent the time-dependency, Time-Expanded Network (TEN), which contains a copy to the set of nodes in the underlying static network for each discrete time step, is introduced. In addition, it is hard for the tourist to set satisfaction values at sightseeing sites numerically, but it is not difficult to classify sightseeing sites into several groups. Therefore, the objective function of our proposed model is set to maximize the total visiting sightseeing sites in each group. The problem is a multiobjective programming problem, and hence, a principle of compromise is introduced to solve our proposed problem in network optimization. Furthermore, the main constrained network optimization problem is equivalently transformed into a recursive equation, and an exact and greedy algorithm is developed. © 2014 IEEE.

关键词： Multiobjective optimization

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Route planning problem with groups of sightseeing sites classified by tourist's sensitivity under Time-Expanded Network

Route planning problem with groups of sightseeing sites clas...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Takashi Hasuike Hideki Katagiri Hiroe Tsubaki Hiroshi Tsuda Graduate School of Information Science and Technology Osaka University Osaka Japan Graduate School of Engineering Hiroshima University Hiroshima Japan Department of Data Science The Institute of Statistical Mathematics Tachikawa Tokyo Japan Faculty of Science and Engineering Doshisha University Kyotanabe Kyoto Japan

This paper proposes a sightseeing route planning problem with time-dependent traveling times among sightseeing sites. Since traveling times are dependent on the time of day, Time-Expanded Network (TEN), which contains a copy to the set of nodes in the underlying static network for each discrete time step, is introduced. In addition, it is hard to set satisfaction values at sightseeing sites numerically due to tourist's ambiguous sensitivity, but it is not difficult to classify sightseeing sites into several groups by the tourist. Therefore, the objective function of our proposed model is set to maximize the total visiting sightseeing sites in each group. The problem is a multiobjective programming problem, and hence, a principle of compromise is introduced to solve our proposed problem in network optimization. Furthermore, a strict algorithm is also developed to equivalently transform the main problem into the existing TEN-based problem.

关键词： Planning Optimization Mathematical model Algorithm design and analysis Heuristic algorithms Programming Indexes

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Sightseeing route planning problem by electric vehicle on the Time-Expanded Network

Sightseeing route planning problem by electric vehicle on th...

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IEEE International Workshop on Computational Intelligence & Applications (IWCIA)

作者： Takashi Hasuike Hiroe Tsubaki Hideki Katagiri Hiroshi Tsuda Graduate School of Information Science and Technology Osaka University Japan Suita Osaka Japan Department of Data Science Institute of Statistical Mathematics Japan Tachikawa Tokyo Japan Graduate School of Engineering Hiroshima University Japan Higashi-Hiroshima Hiroshima Japan Faculty of Science and Engineering Doshisha University Japan Kyotanabe Kyoto Japan

关键词： Planning Optimization Mathematical model Batteries Indexes Linear programming Heuristic algorithms

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

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