Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Cost complexity-based pruning of ensemble classifiers
Knowledge and Information Systems
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
Clustering ensembles of neural network models
Neural Networks
A Monte Carlo analysis of ensemble classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Switching class labels to generate classification ensembles
Pattern Recognition
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Using Boosting to prune Double-Bagging ensembles
Computational Statistics & Data Analysis
Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Large-scale multimedia semantic concept modeling using robust subspace bagging and MapReduce
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Selection of decision stumps in bagging ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Iterative reordering of rules for building ensembles without relearning
DS'07 Proceedings of the 10th international conference on Discovery science
Ensembles of jittered association rule classifiers
Data Mining and Knowledge Discovery
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Storage device performance prediction with selective bagging classification and regression tree
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Greedy optimization classifiers ensemble based on diversity
Pattern Recognition
Ensemble pruning via base-classifier replacement
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Margin distribution based bagging pruning
Neurocomputing
Pruning adaptive boosting ensembles by means of a genetic algorithm
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Expert pruning based on genetic algorithm in regression problems
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Selective ensemble of support vector data descriptions for novelty detection
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Margin-based ordered aggregation for ensemble pruning
Pattern Recognition Letters
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
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We present a novel ensemble pruning method based on reordering the classifiers obtained from bagging and then selecting a subset for aggregation. Ordering the classifiers generated in bagging makes it possible to build subensembles of increasing size by including first those classifiers that are expected to perform best when aggregated. Ensemble pruning is achieved by halting the aggregation process before all the classifiers generated are included into the ensemble. Pruned subensembles containing between 15% and 30% of the initial pool of classifiers, besides being smaller, improve the generalization performance of the full bagging ensemble in the classification problems investigated.