The Strength of Weak Learnability
Machine Learning
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Optimal linear combinations of neural networks
Optimal linear combinations of neural networks
Machine Learning
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting regression estimators
Neural Computation
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Ensemble learning via negative correlation
Neural Networks
Randomizing Outputs to Increase Prediction Accuracy
Machine Learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
Using Iterated Bagging to Debias Regressions
Machine Learning
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
A Principal Components Approach to Combining Regression Estimates
Machine Learning
Boosting Methods for Regression
Machine Learning
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
Combining Classifiers with Meta Decision Trees
Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Improving nonparametric regression methods by bagging and boosting
Computational Statistics & Data Analysis - Nonlinear methods and data mining
On the Boosting Pruning Problem
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Adaptive Selection of Image Classifiers
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
A Dynamic Integration Algorithm for an Ensemble of Classifiers
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Methods for Designing Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Clustering ensembles of neural network models
Neural Networks
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Classification and regression by combining models
Classification and regression by combining models
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Core Vector Regression for very large regression problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with AdaBoost.RT, an improved boosting scheme for regression
Neural Computation
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Using boosting to prune bagging ensembles
Pattern Recognition Letters
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Data Mining Using SAS Enterprise Miner (Wiley Series in Computational Statistics)
Data Mining Using SAS Enterprise Miner (Wiley Series in Computational Statistics)
Dynamic integration of classifiers for handling concept drift
Information Fusion
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
Engineering multiversion neural-net systems
Neural Computation
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Cocktail Ensemble for Regression
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Computational Statistics & Data Analysis
A fast ensemble pruning algorithm based on pattern mining process
Data Mining and Knowledge Discovery
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Ensemble methods for improving the performance of neighborhood-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Neural network ensembles: evaluation of aggregation algorithms
Artificial Intelligence
A Multi-agent System to Assist with Real Estate Appraisals Using Bagging Ensembles
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Artificial Intelligence Review
An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems
The Journal of Machine Learning Research
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
Iterative reordering of rules for building ensembles without relearning
DS'07 Proceedings of the 10th international conference on Discovery science
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Ensembles of jittered association rule classifiers
Data Mining and Knowledge Discovery
Skew estimation of document images using bagging
IEEE Transactions on Image Processing
Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Ensembles of nearest neighbor forecasts
ECML'06 Proceedings of the 17th European conference on Machine Learning
Diversified SVM ensembles for large data sets
ECML'06 Proceedings of the 17th European conference on Machine Learning
Dynamic integration with random forests
ECML'06 Proceedings of the 17th European conference on Machine Learning
Improving on bagging with input smearing
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Infinite ensemble learning with support vector machines
ECML'05 Proceedings of the 16th European conference on Machine Learning
An experiment with association rules and classification: post-bagging and conviction
DS'05 Proceedings of the 8th international conference on Discovery Science
Adaptive radius immune algorithm for data clustering
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
GAB-EPA: a GA based ensemble pruning approach to tackle multiclass imbalanced problems
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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The goal of ensemble regression is to combine several models in order to improve the prediction accuracy in learning problems with a numerical target variable. The process of ensemble learning can be divided into three phases: the generation phase, the pruning phase, and the integration phase. We discuss different approaches to each of these phases that are able to deal with the regression problem, categorizing them in terms of their relevant characteristics and linking them to contributions from different fields. Furthermore, this work makes it possible to identify interesting areas for future research.