The Ensemble and Model Comparison Approaches for Big Data Analytics in Social Sciences.
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Chong Ho Yu | Hyun Seo Lee | Emily Lara | Siyan Gan | Chong Ho Alex Yu | Hyun Seo Lee | Siyan Gan | Emily Lara
[1] Carlos J. Costa,et al. Data visualization , 2015, CDQR.
[2] Robert Tibshirani,et al. An Introduction to the Bootstrap , 1994 .
[3] C. I. Mosier. I. Problems and Designs of Cross-Validation 1 , 1951 .
[4] Taghi M. Khoshgoftaar,et al. Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[5] Fabio Roli,et al. Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation , 2005, Multiple Classifier Systems.
[6] Faisal Zaman,et al. Classification Performance of Bagging and Boosting Type Ensemble Methods with Small Training Sets , 2011, New Generation Computing.
[7] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[8] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[9] Gitta H. Lubke,et al. Finding structure in data using multivariate tree boosting , 2015, Psychological methods.
[10] Chong Ho Yu. Dancing with the Data: The Art and Science of Data Visualization , 2014 .
[11] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[12] Donald K. Wedding,et al. Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..
[13] Yuhong Yang. Can the Strengths of AIC and BIC Be Shared , 2005 .
[14] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[15] Mike W.-L. Cheung,et al. Analyzing Big Data in Psychology: A Split/Analyze/Meta-Analyze Approach , 2016, Front. Psychol..
[16] Lior Rokach,et al. Ensemble-based classifiers , 2010, Artificial Intelligence Review.
[17] C. B. Colby. The weirdest people in the world , 1973 .
[18] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.
[19] David Hinkley,et al. Bootstrap Methods: Another Look at the Jackknife , 2008 .
[20] Eric R. Zieyel. The Collected Works of John W. Tukey , 1988 .
[21] Kenneth C. Lichtendahl,et al. Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner , 2016 .
[22] R. Suganya,et al. Data Mining Concepts and Techniques , 2010 .
[23] John W. Tukey,et al. Exploratory Data Analysis. , 1979 .
[24] Jure Leskovec,et al. Mining big data to extract patterns and predict real-life outcomes. , 2016, Psychological methods.
[25] Sotiris B. Kotsiantis,et al. Bagging and boosting variants for handling classifications problems: a survey , 2013, The Knowledge Engineering Review.
[26] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[27] D. Opitz,et al. Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..
[28] P. Bühlmann,et al. Analyzing Bagging , 2001 .
[29] Robert P. W. Duin,et al. Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.
[30] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[31] Gunnar Rätsch,et al. An Introduction to Boosting and Leveraging , 2002, Machine Learning Summer School.
[32] David R. Anderson,et al. Multimodel Inference , 2004 .
[33] L. Breiman. Arcing Classifiers , 1998 .
[34] Alan H. Fielding,et al. Cluster and Classification Techniques for the Biosciences , 2006 .
[35] Andrew B. Collmus,et al. A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. , 2016, Psychological methods.
[36] Christophe Croux,et al. Bagging and Boosting Classification Trees to Predict Churn , 2006 .
[37] Andrew Dean Ho,et al. Big Data Analysis in Higher Education: Promises and Pitfalls , 2016 .
[38] Chun-Xia Zhang,et al. Investigating the Effect of Randomly Selected Feature Subsets on Bagging and Boosting , 2015, Commun. Stat. Simul. Comput..
[39] G DietterichThomas. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .
[40] Shuo-Yan Chou,et al. Enhancing the classification accuracy by scatter-search-based ensemble approach , 2011, Appl. Soft Comput..
[41] A. K. Kurtz. A Research Test of the Rorschach Test , 1948 .
[42] B. Efron. The jackknife, the bootstrap, and other resampling plans , 1987 .
[43] R. Rosenthal,et al. Statistical Procedures and the Justification of Knowledge in Psychological Science , 1989 .
[44] Sandip Sinharay,et al. An NCME Instructional Module on Data Mining Methods for Classification and Regression , 2016 .
[45] B. Efron. Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods , 1981 .
[46] Szymon Jaroszewicz,et al. Ensemble methods for uplift modeling , 2014, Data Mining and Knowledge Discovery.
[47] Shigeyuki Hamori,et al. Ensemble Learning or Deep Learning? Application to Default Risk Analysis , 2018 .
[48] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[49] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[50] D. Krus,et al. Computer Assisted Multicrossvalidation in Regression Analysis , 1982 .