Foundations of Ensemble Learning
暂无分享,去创建一个
[1] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[2] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[3] Abbas Mardani,et al. Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014 , 2015 .
[4] José Ramón Quevedo,et al. Using ensembles for problems with characterizable changes in data distribution: A case study on quantification , 2017, Inf. Fusion.
[5] Feng Yang,et al. Robust Feature Selection for Microarray Data Based on Multicriterion Fusion , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[6] Marcel Dettling,et al. BagBoosting for tumor classification with gene expression data , 2004, Bioinform..
[7] P. Bühlmann,et al. Boosting With the L2 Loss , 2003 .
[8] Lawrence O. Hall,et al. A scalable framework for cluster ensembles , 2009, Pattern Recognit..
[9] Aurélien Garivier,et al. On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models , 2014, J. Mach. Learn. Res..
[10] Ludmila I. Kuncheva,et al. Combining Pattern Classifiers: Methods and Algorithms , 2004 .
[11] Lawrence O. Hall,et al. A Cluster Ensemble Framework for Large Data sets , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.
[12] Yoav Freund,et al. Boosting a weak learning algorithm by majority , 1995, COLT '90.
[13] J. Ross Quinlan,et al. Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.
[14] Thomas L. Saaty. What is the analytic hierarchy process , 1988 .
[15] Verónica Bolón-Canedo,et al. Data classification using an ensemble of filters , 2014, Neurocomputing.
[16] Kai Ming Ting,et al. A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.
[17] Edmundas Kazimieras Zavadskas,et al. Developing a new hybrid MCDM method for selection of the optimal alternative of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents , 2013 .
[18] Hamido Fujita,et al. Incremental fuzzy cluster ensemble learning based on rough set theory , 2017, Knowl. Based Syst..
[19] José Luis Alba-Castro,et al. Double-base asymmetric AdaBoost , 2013, Neurocomputing.
[20] Chun-Xia Zhang,et al. A local boosting algorithm for solving classification problems , 2008, Comput. Stat. Data Anal..
[21] Gwo-Hshiung Tzeng,et al. Exploring smart phone improvements based on a hybrid MCDM model , 2014, Expert Syst. Appl..
[22] Terry Windeatt,et al. Embedded Feature Ranking for Ensemble MLP Classifiers , 2011, IEEE Transactions on Neural Networks.
[23] Peter A. Flach,et al. Machine Learning - The Art and Science of Algorithms that Make Sense of Data , 2012 .
[24] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[25] Philip S. Yu,et al. Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.
[26] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[27] Verónica Bolón-Canedo,et al. Ensemble feature selection: Homogeneous and heterogeneous approaches , 2017, Knowl. Based Syst..
[28] Rich Caruana,et al. An empirical comparison of supervised learning algorithms , 2006, ICML.
[29] Verónica Bolón-Canedo,et al. An ensemble of filters and classifiers for microarray data classification , 2012, Pattern Recognit..
[30] Thomas G. Dietterich,et al. Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..
[31] Paul A. Viola,et al. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.
[32] Cesare Furlanello,et al. Parallelizing AdaBoost by weights dynamics , 2007, Comput. Stat. Data Anal..
[33] J. Skilling. Bayesian Methods in Cosmology: Foundations and algorithms , 2009 .
[34] Nikunj C. Oza,et al. Online Ensemble Learning , 2000, AAAI/IAAI.
[35] Sandro Vega-Pons,et al. A Survey of Clustering Ensemble Algorithms , 2011, Int. J. Pattern Recognit. Artif. Intell..
[36] Joydeep Ghosh,et al. Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..
[37] Kurt Hornik,et al. A Cluster Ensembles Framework , 2003, HIS.
[38] B. Peter. BOOSTING FOR HIGH-DIMENSIONAL LINEAR MODELS , 2006 .
[39] Brendan J. Frey,et al. Are Random Forests Truly the Best Classifiers? , 2016, J. Mach. Learn. Res..
[40] Max Bramer,et al. Principles of Data Mining , 2013, Undergraduate Topics in Computer Science.
[41] John W. Tukey,et al. Exploratory data analysis , 1977, Addison-Wesley series in behavioral science : quantitative methods.
[42] Peter A. Flach,et al. Cost-sensitive boosting algorithms: Do we really need them? , 2016, Machine Learning.
[43] Lior Rokach,et al. Ensemble-based classifiers , 2010, Artificial Intelligence Review.
[44] Ludmila I. Kuncheva,et al. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.
[45] P. Bühlmann. Boosting for high-dimensional linear models , 2006 .
[46] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.
[47] M. Karthikeyan,et al. Risk analysis and warning rate of hot environment for foundry industry using hybrid MCDM technique , 2015 .
[48] Mohammed Attik. Using Ensemble Feature Selection Approach in Selecting Subset with Relevant Features , 2006, ISNN.
[49] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[50] Yvan Saeys,et al. Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.
[51] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[52] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[53] Leo Breiman,et al. Random Forests , 2001, Machine Learning.