Ensemble approaches for regression: A survey
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Alípio Mário Jorge | João Mendes-Moreira | Jorge Freire de Sousa | Carlos Soares | A. Jorge | João Mendes-Moreira | C. Soares
[1] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[2] Thomas G. Dietterich,et al. Pruning Adaptive Boosting , 1997, ICML.
[3] Jihoon Yang,et al. Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..
[4] Nathan Intrator,et al. Boosting Regression Estimators , 1999, Neural Computation.
[5] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[6] Peter Bühlmann,et al. Bagging, Boosting and Ensemble Methods , 2012 .
[7] Jörg D. Wichard,et al. Building Ensembles with Heterogeneous Models , 2003 .
[8] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[9] Gian Luca Marcialis,et al. A study on the performances of dynamic classifier selection based on local accuracy estimation , 2005, Pattern Recognit..
[10] Bruce E. Rosen,et al. Ensemble Learning Using Decorrelated Neural Networks , 1996, Connect. Sci..
[11] Randall Matignon. Data Mining Using SAS® Enterprise Miner™: Matignon/Data Mining , 2007 .
[12] Gonzalo Martínez-Muñoz,et al. Pruning in ordered bagging ensembles , 2006, ICML.
[13] Joydeep Ghosh,et al. Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..
[14] Bernhard Pfahringer,et al. Improving on Bagging with Input Smearing , 2006, PAKDD.
[15] Gonzalo Mart,et al. Pruning in Ordered Regression Bagging Ensembles , 2006 .
[16] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[17] Pedro M. Domingos. Why Does Bagging Work? A Bayesian Account and its Implications , 1997, KDD.
[18] Alípio Mário Jorge,et al. An Experiment with Association Rules and Classification: Post-Bagging and Conviction , 2005, Discovery Science.
[19] David W. Opitz,et al. Generating Accurate and Diverse Members of a Neural-Network Ensemble , 1995, NIPS.
[20] Mykola Pechenizkiy,et al. Dynamic Integration with Random Forests , 2006, ECML.
[21] Thomas G. Dietterich. Machine-Learning Research , 1997, AI Mag..
[22] Tony R. Martinez,et al. Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..
[23] Alexey Tsymbal,et al. A Dynamic Integration Algorithm for an Ensemble of Classifiers , 1999, ISMIS.
[24] Philip S. Yu,et al. Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.
[25] Michael J. Pazzani,et al. A Principal Components Approach to Combining Regression Estimates , 1999, Machine Learning.
[26] Rich Caruana,et al. Ensemble selection from libraries of models , 2004, ICML.
[27] J. Friedman. Stochastic gradient boosting , 2002 .
[28] Daniel Hernández-Lobato,et al. Pruning in Ordered Regression Bagging Ensembles , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[29] Ivor W. Tsang,et al. Diversified SVM Ensembles for Large Data Sets , 2006, ECML.
[30] João Mendes Moreira,et al. An ensemble regression approach for bus trip time prediction , 2006 .
[31] Kurt Hornik,et al. The support vector machine under test , 2003, Neurocomputing.
[32] Yunping Zou,et al. Embedded neural network to model-based Permanent Magnet Synchronous Motor diagnostics , 2009, 2009 IEEE 6th International Power Electronics and Motion Control Conference.
[33] Kenneth DeJong,et al. Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).
[34] Xin Yao,et al. Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..
[35] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[36] Daniel Hernández-Lobato,et al. An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] Naonori Ueda,et al. Generalization error of ensemble estimators , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[38] Durga L. Shrestha,et al. Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression , 2006, Neural Computation.
[39] Volker Tresp,et al. Combining Estimators Using Non-Constant Weighting Functions , 1994, NIPS.
[40] Xiaoyu Chu,et al. Predicting changes in protein thermostability brought about by single- or multi-site mutations , 2010, BMC Bioinformatics.
[41] Xin Yao,et al. Ensemble learning via negative correlation , 1999, Neural Networks.
[42] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[43] Leo Breiman,et al. Using Iterated Bagging to Debias Regressions , 2001, Machine Learning.
[44] Ian Witten,et al. Data Mining , 2000 .
[45] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[46] Nathan Intrator,et al. Bootstrapping with Noise: An Effective Regularization Technique , 1996, Connect. Sci..
[47] Anil K. Jain,et al. Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[48] David B. Skalak,et al. Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.
[49] Alexey Tsymbal,et al. Dynamic Integration of Regression Models , 2004, Multiple Classifier Systems.
[50] Padraig Cunningham,et al. Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error , 2001, ECML.
[51] Lior Rokach,et al. Ensemble-based classifiers , 2010, Artificial Intelligence Review.
[52] Ludmila I. Kuncheva,et al. Combining Pattern Classifiers: Methods and Algorithms , 2004 .
[53] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[54] Fernando José Von Zuben,et al. Adaptive Radius Immune Algorithm for Data Clustering , 2005, ICARIS.
[55] Nanning Zheng,et al. Skew Estimation of Document Images Using Bagging , 2010, IEEE Transactions on Image Processing.
[56] Sherif Hashem,et al. Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.
[57] Manfred M. Fischer,et al. Neural network ensembles and their application to traffic flow prediction in telecommunications networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[58] Geoff Holmes,et al. New ensemble methods for evolving data streams , 2009, KDD.
[59] Josef Kittler,et al. Floating search methods for feature selection with nonmonotonic criterion functions , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).
[60] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[61] Toniann Pitassi,et al. A Gradient-Based Boosting Algorithm for Regression Problems , 2000, NIPS.
[62] Ahmed Al-Ani,et al. Feature Subset Selection Using Ant Colony Optimization , 2008 .
[63] Gunnar Rätsch,et al. Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces , 2002, Machine Learning.
[64] Matti Aksela,et al. Comparison of Classifier Selection Methods for Improving Committee Performance , 2003, Multiple Classifier Systems.
[65] John Loughrey,et al. Using Early Stopping to Reduce Overfitting in Wrapper-Based Feature Weighting , 2005 .
[66] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .
[67] John F. Kolen,et al. Backpropagation is Sensitive to Initial Conditions , 1990, Complex Syst..
[68] Wei Tang,et al. Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..
[69] João Pedro Carvalho Leal Mendes Moreira,et al. Travel time prediction for the planning of mass transit companies: a machine learning approach , 2008 .
[70] Ivor W. Tsang,et al. Core Vector Regression for very large regression problems , 2005, ICML.
[71] Christopher J. Merz,et al. Dynamical Selection of Learning Algorithms , 1995, AISTATS.
[72] Randall Matignon,et al. Data Mining Using SAS Enterprise Miner , 2007 .
[73] Chun-Xia Zhang,et al. An empirical study of using Rotation Forest to improve regressors , 2008, Appl. Math. Comput..
[74] Fabio Roli,et al. Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..
[75] Mykola Pechenizkiy,et al. Dynamic integration of classifiers for handling concept drift , 2008, Inf. Fusion.
[76] Michael J. Pazzani,et al. Classification and regression by combining models , 1998 .
[77] William B. Yates,et al. Engineering Multiversion Neural-Net Systems , 1996, Neural Computation.
[78] L. Breiman. Heuristics of instability and stabilization in model selection , 1996 .
[79] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[80] Lluís A. Belanche Muñoz,et al. Feature selection algorithms: a survey and experimental evaluation , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[81] R. Tibshirani,et al. Combining Estimates in Regression and Classification , 1996 .
[82] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[83] Alípio Mário Jorge,et al. Ensembles of jittered association rule classifiers , 2010, Data Mining and Knowledge Discovery.
[84] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[85] Qiang-Li Zhao,et al. A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process , 2009, ECML/PKDD.
[86] Ling Li,et al. Infinite Ensemble Learning with Support Vector Machines , 2005, ECML.
[87] David W. Opitz,et al. Feature Selection for Ensembles , 1999, AAAI/IAAI.
[88] Elizabeth Shriberg,et al. An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems , 2009, J. Mach. Learn. Res..
[89] Alípio Mário Jorge,et al. Comparing state-of-the-art regression methods for long term travel time prediction , 2012, Intell. Data Anal..
[90] Ludmila I. Kuncheva,et al. Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[91] Pablo M. Granitto,et al. Neural network ensembles: evaluation of aggregation algorithms , 2005, Artif. Intell..
[92] Tao Xiong,et al. A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[93] Eamonn J. Keogh,et al. Ensembles of Nearest Neighbor Forecasts , 2006, ECML.
[94] Yang Yu,et al. Cocktail Ensemble for Regression , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[95] Paul W. Munro,et al. Reducing Variance of Committee Prediction with Resampling Techniques , 1996, Connect. Sci..
[96] Hendrik Blockeel,et al. Experiment Databases , 2007, Inductive Databases and Constraint-Based Data Mining.
[97] 共立出版株式会社. コンピュータ・サイエンス : ACM computing surveys , 1978 .
[98] Elaine J. Weyuker,et al. Comparing the effectiveness of several modeling methods for fault prediction , 2010, Empirical Software Engineering.
[99] Guoyin Wang,et al. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing , 2013, Lecture Notes in Computer Science.
[100] Saso Dzeroski,et al. Combining Classifiers with Meta Decision Trees , 2003, Machine Learning.
[101] Jon Louis Bentley,et al. Multidimensional binary search trees used for associative searching , 1975, CACM.
[102] Nikunj C. Oza,et al. Online Ensemble Learning , 2000, AAAI/IAAI.
[103] Agostino Di Ciaccio,et al. Improving nonparametric regression methods by bagging and boosting , 2002 .
[104] Carlos Soares,et al. Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach , 2009, MLDM.
[105] Leo Breiman,et al. Stacked regressions , 2004, Machine Learning.
[106] P. Stark. Bounded-Variable Least-Squares: an Algorithm and Applications , 2008 .
[107] Kevin W. Bowyer,et al. Combination of multiple classifiers using local accuracy estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[108] Marcus A. Maloof,et al. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..
[109] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[110] Thomas G. Dietterich. Machine-Learning Research Four Current Directions , 1997 .
[111] Robert Sabourin,et al. From dynamic classifier selection to dynamic ensemble selection , 2008, Pattern Recognit..
[112] Randall Matignon. Data Mining Using SAS Enterprise Miner (Wiley Series in Computational Statistics) , 2007 .
[113] Geoffrey I. Webb,et al. Multistrategy ensemble learning: reducing error by combining ensemble learning techniques , 2004, IEEE Transactions on Knowledge and Data Engineering.
[114] N. Garc'ia-Pedrajas,et al. CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features , 2005, J. Artif. Intell. Res..
[115] Lior Rokach,et al. Random Projection Ensemble Classifiers , 2009, ICEIS.
[116] Alípio Mário Jorge,et al. Iterative Reordering of Rules for Building Ensembles Without Relearning , 2007, Discovery Science.
[117] L. Cooper,et al. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .
[118] Christino Tamon,et al. On the Boosting Pruning Problem , 2000, ECML.
[119] Leo Breiman,et al. Randomizing Outputs to Increase Prediction Accuracy , 2000, Machine Learning.
[120] Zbigniew Telec,et al. A Multi-agent System to Assist with Real Estate Appraisals Using Bagging Ensembles , 2009, ICCCI.
[121] Harris Drucker,et al. Improving Regressors using Boosting Techniques , 1997, ICML.
[122] Marko Robnik-Sikonja,et al. Improving Random Forests , 2004, ECML.
[123] Antanas Verikas,et al. Soft combination of neural classifiers: A comparative study , 1999, Pattern Recognit. Lett..
[124] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[125] César Hervás-Martínez,et al. Cooperative coevolution of artificial neural network ensembles for pattern classification , 2005, IEEE Transactions on Evolutionary Computation.
[127] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[128] Lior Rokach,et al. Pattern Classification Using Ensemble Methods , 2009, Series in Machine Perception and Artificial Intelligence.
[129] Fabio Roli,et al. Methods for Designing Multiple Classifier Systems , 2001, Multiple Classifier Systems.
[130] Tom Heskes,et al. Clustering ensembles of neural network models , 2003, Neural Networks.
[131] Amnon Meisels,et al. Ensemble methods for improving the performance of neighborhood-based collaborative filtering , 2009, RecSys '09.
[132] Robert Tibshirani,et al. Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[133] Ricardo Vilalta,et al. Metalearning - Applications to Data Mining , 2008, Cognitive Technologies.
[134] A. Buja,et al. OBSERVATIONS ON BAGGING , 2006 .
[135] Luca Didaci,et al. Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule , 2004, Multiple Classifier Systems.
[136] Niall Rooney,et al. A weighted combination of stacking and dynamic integration , 2007, Pattern Recognit..
[137] Xin Yao,et al. A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.
[138] Vasile Palade,et al. Multi-Classifier Systems: Review and a roadmap for developers , 2006, Int. J. Hybrid Intell. Syst..
[139] RanawanaRomesh,et al. Multi-Classifier Systems: Review and a roadmap for developers , 2006 .
[140] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[141] Peter Tiño,et al. Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..
[142] Wei Tang,et al. Selective Ensemble of Decision Trees , 2003, RSFDGrC.
[143] Naveen Aggarwal,et al. Content Management System Effort Estimation Using Bagging Predictors , 2008, EIAT/IETA.
[144] Philippe Flajolet,et al. Adaptive Sampling , 1997 .
[145] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[146] Jill P. Mesirov,et al. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.
[147] Marko Robnik-Sikonja,et al. Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.
[148] Carlotta Domeniconi,et al. Nearest neighbor ensemble , 2004, ICPR 2004.
[149] Hyun-Chul Kim,et al. Constructing support vector machine ensemble , 2003, Pattern Recognit..
[150] David P. Helmbold,et al. Boosting Methods for Regression , 2002, Machine Learning.
[151] David W. Aha,et al. A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.
[152] E. L. Lehmann,et al. Theory of point estimation , 1950 .
[153] Fernando José Von Zuben,et al. The Influence of the Pool of Candidates on the Performance of Selection and Combination Techniques in Ensembles , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[154] Byung Ro Moon,et al. Hybrid Genetic Algorithms for Feature Selection , 2004, IEEE Trans. Pattern Anal. Mach. Intell..
[155] HoTin Kam. The Random Subspace Method for Constructing Decision Forests , 1998 .
[156] Zoran Obradovic,et al. Effective pruning of neural network classifier ensembles , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[157] Xing Wu,et al. Research on ensemble learning based on discretization method , 2008, 2008 9th International Conference on Signal Processing.
[158] Lior Rokach,et al. Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography , 2009, Comput. Stat. Data Anal..
[159] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[160] Lior Rokach,et al. Collective-agreement-based pruning of ensembles , 2009, Comput. Stat. Data Anal..
[161] Gavin Brown,et al. Diversity in neural network ensembles , 2004 .
[162] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[163] Bogdan Gabrys,et al. Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting , 2001, Multiple Classifier Systems.
[164] Fabio Roli,et al. Adaptive Selection of Image Classifiers , 1997, ICIAP.
[165] Gonzalo Martínez-Muñoz,et al. Using boosting to prune bagging ensembles , 2007, Pattern Recognit. Lett..