Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce
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Francisco Herrera | Min Chen | Sergio Ramírez-Gallego | Salvador García | Alberto Fernández | S. García | F. Herrera | S. Ramírez-Gallego | Alberto Fernández | Min Chen
[1] Andrew Musselman. Apache Mahout , 2019, Encyclopedia of Big Data Technologies.
[2] Fabian Hueske,et al. Apache Flink , 2019, Encyclopedia of Big Data Technologies.
[3] Verónica Bolón-Canedo,et al. An Information Theory-Based Feature Selection Framework for Big Data Under Apache Spark , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[4] Deepak Choudhary,et al. Internet of things: A survey on enabling technologies, application and standardization , 2018 .
[5] João Gama,et al. Ensemble learning for data stream analysis: A survey , 2017, Inf. Fusion.
[6] Shiliang Sun,et al. A review of natural language processing techniques for opinion mining systems , 2017, Inf. Fusion.
[7] Min Chen,et al. Disease Prediction by Machine Learning Over Big Data From Healthcare Communities , 2017, IEEE Access.
[8] Francisco Herrera,et al. A comparison on scalability for batch big data processing on Apache Spark and Apache Flink , 2017 .
[9] Francisco Herrera,et al. An insight into imbalanced Big Data classification: outcomes and challenges , 2017 .
[10] Verónica Bolón-Canedo,et al. Fast‐mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High‐Dimensional Big Data , 2017, Int. J. Intell. Syst..
[11] Francisco Herrera,et al. kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data , 2017, Knowl. Based Syst..
[12] Min Chen. Welcome to the New Interdisciplinary Journal Combining Big Data and Cognitive Computing , 2017, Big Data Cogn. Comput..
[13] Min Chen,et al. Big-Data Analytics for Cloud, IoT and Cognitive Computing , 2017 .
[14] S. R,et al. Data Mining with Big Data , 2017, 2017 11th International Conference on Intelligent Systems and Control (ISCO).
[15] Hing Kai Chan,et al. Recent Development in Big Data Analytics for Business Operations and Risk Management , 2017, IEEE Transactions on Cybernetics.
[16] Verónica Bolón-Canedo,et al. An Information Theoretic Feature Selection Framework for Big Data under Apache Spark , 2016, ArXiv.
[17] Victor Chang,et al. A review and future direction of agile, business intelligence, analytics and data science , 2016, Int. J. Inf. Manag..
[18] María José del Jesús,et al. A View on Fuzzy Systems for Big Data: Progress and Opportunities , 2016, Int. J. Comput. Intell. Syst..
[19] Roger H. L. Chiang,et al. Big Data Research in Information Systems: Toward an Inclusive Research Agenda , 2016, J. Assoc. Inf. Syst..
[20] Jason J. Jung,et al. Social big data: Recent achievements and new challenges , 2015, Information Fusion.
[21] Ameet Talwalkar,et al. MLlib: Machine Learning in Apache Spark , 2015, J. Mach. Learn. Res..
[22] Dr. Tamanna Siddiqui,et al. BIG DATA ANALYTICS ON THE CLOUD , 2016 .
[23] Lior Rokach,et al. Decision forest: Twenty years of research , 2016, Inf. Fusion.
[24] Cheng Soon Ong,et al. Multivariate spearman's ρ for aggregating ranks using copulas , 2016 .
[25] Jorge A. Balazs,et al. Opinion Mining and Information Fusion: A survey , 2016, Inf. Fusion.
[26] Jing Zhang,et al. Parallel information fusion method for microarray data analysis , 2015, 2015 IEEE International Conference on Big Data (Big Data).
[27] Francisco Herrera,et al. An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species , 2015, BioMed research international.
[28] Mohsen Guizani,et al. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.
[29] Murtaza Haider,et al. Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..
[30] Patrick Wendell,et al. Learning Spark: Lightning-Fast Big Data Analytics , 2015 .
[31] Francisco Herrera,et al. MRPR: A MapReduce solution for prototype reduction in big data classification , 2015, Neurocomputing.
[32] Francisco Herrera,et al. A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules , 2015, Int. J. Comput. Intell. Syst..
[33] Gang Li,et al. Big data related technologies, challenges and future prospects , 2015, J. Inf. Technol. Tour..
[34] Francisco Herrera,et al. Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data , 2015, Fuzzy Sets Syst..
[35] Francisco Herrera,et al. On the use of MapReduce for imbalanced big data using Random Forest , 2014, Inf. Sci..
[36] Bin Wu,et al. Parallelization of ontology construction and fusion based on MapReduce , 2014, 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems.
[37] Helmut Krcmar,et al. Big Data , 2014, Wirtschaftsinf..
[38] Thomas Hofmann,et al. Communication-Efficient Distributed Dual Coordinate Ascent , 2014, NIPS.
[39] María José del Jesús,et al. Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks , 2014, WIREs Data Mining Knowl. Discov..
[40] C. L. Philip Chen,et al. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..
[41] Yonggang Wen,et al. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial , 2014, IEEE Access.
[42] Felix Naumann,et al. The Stratosphere platform for big data analytics , 2014, The VLDB Journal.
[43] P. Baldi,et al. Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.
[44] Babita Gupta,et al. The Current State of Business Intelligence in Academia: The Arrival of Big Data , 2014, CAIS.
[45] Laurence T. Yang,et al. Data Mining for Internet of Things: A Survey , 2014, IEEE Communications Surveys & Tutorials.
[46] Limsoon Wong,et al. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes , 2013, BMC Bioinformatics.
[47] Gilles Louppe,et al. Independent consultant , 2013 .
[48] Joaquim Assunção,et al. Distributed Stochastic Aware Random Forests -- Efficient Data Mining for Big Data , 2013, 2013 IEEE International Congress on Big Data.
[49] R. Khan,et al. Use of DAG in Distributed Parallel Computing , 2013 .
[50] Michael Minelli,et al. Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses , 2012 .
[51] Veda C. Storey,et al. Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..
[52] Indranil Palit,et al. Scalable and Parallel Boosting with MapReduce , 2012, IEEE Transactions on Knowledge and Data Engineering.
[53] Astrid Rheinländer,et al. Opening the Black Boxes in Data Flow Optimization , 2012, Proc. VLDB Endow..
[54] Volker Markl,et al. Spinning Fast Iterative Data Flows , 2012, Proc. VLDB Endow..
[55] Michael J. Franklin,et al. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.
[56] Yon Dohn Chung,et al. Parallel data processing with MapReduce: a survey , 2012, SGMD.
[57] Francisco Herrera,et al. A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[58] Sean Owen,et al. Mahout in Action , 2011 .
[59] Chuck Lam,et al. Hadoop in Action , 2010 .
[60] Scott Shenker,et al. Spark: Cluster Computing with Working Sets , 2010, HotCloud.
[61] Hairong Kuang,et al. The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).
[62] Sanjay Ghemawat,et al. MapReduce: a flexible data processing tool , 2010, CACM.
[63] Klaus Nordhausen,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .
[64] Qing He,et al. Parallel K-Means Clustering Based on MapReduce , 2009, CloudCom.
[65] Roberto J. Bayardo,et al. PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce , 2009, Proc. VLDB Endow..
[66] Tom White,et al. Hadoop: The Definitive Guide , 2009 .
[67] Sanjay Ghemawat,et al. MapReduce: simplified data processing on large clusters , 2008, CACM.
[68] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[69] Ludmila I. Kuncheva. Diversity in multiple classifier systems , 2005, Inf. Fusion.
[70] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[71] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[72] Robert E. Schapire,et al. A Brief Introduction to Boosting , 1999, IJCAI.
[73] Yoram Singer,et al. Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.
[74] Leslie G. Valiant,et al. A bridging model for parallel computation , 1990, CACM.
[75] Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
[76] Juha Heinanen,et al. OF DATA INTENSIVE APPLICATIONS , 1986 .