Communications of the ACM
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
On the Boosting Pruning Problem
ECML '00 Proceedings of the 11th European Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd international conference on Machine learning
An analysis of diversity measures
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Information Theoretic Perspective on Multiple Classifier Systems
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Selective Ensemble under Regularization Framework
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Predictive Ensemble Pruning by Expectation Propagation
IEEE Transactions on Knowledge and Data Engineering
Multi-information ensemble diversity
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
IEEE Transactions on Information Theory
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Ensemble Methods: Foundations and Algorithms
Ensemble Methods: Foundations and Algorithms
Classifier ensemble using a heuristic learning with sparsity and diversity
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Embedding change rate estimation based on ensemble learning
Proceedings of the first ACM workshop on Information hiding and multimedia security
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Diversity among individual classifiers is recognized to play a key role in ensemble, however, few theoretical properties are known for classification. In this paper, by focusing on the popular ensemble pruning setting (i.e., combining classifier by voting and measuring diversity in pairwise manner), we present a theoretical study on the effect of diversity on the generalization performance of voting in the PAC-learning framework. It is disclosed that the diversity is closely-related to the hypothesis space complexity, and encouraging diversity can be regarded to apply regularization on ensemble methods. Guided by this analysis, we apply explicit diversity regularization to ensemble pruning, and propose the Diversity Regularized Ensemble Pruning (DREP) method. Experimental results show the effectiveness of DREP.