Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Bagging Can Stabilize without Reducing Variance
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Limiting the Number of Trees in Random Forests
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Linear and order statistics combiners for reliable pattern classification
Linear and order statistics combiners for reliable pattern classification
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
An ensemble dependence measure
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Combining bagging, boosting, rotation forest and random subspace methods
Artificial Intelligence Review
A MapReduce-based distributed SVM ensemble for scalable image classification and annotation
Computers & Mathematics with Applications
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In this paper the performance of bagging in classification problems is theoretically analysed, using a framework developed in works by Tumer and Ghosh and extended by the authors. A bias-variance decomposition is derived, which relates the expected misclassification probability attained by linearly combining classifiers trained on N bootstrap replicates of a fixed training set to that attained by a single bootstrap replicate of the same training set. Theoretical results show that the expected misclassification probability of bagging has the same bias component as a single bootstrap replicate, while the variance component is reduced by a factor N. Experimental results show that the performance of bagging as a function of the number of bootstrap replicates follows quite well our theoretical prediction. It is finally shown that theoretical results derived for bagging also apply to other methods for constructing multiple classifiers based on randomisation, such as the random subspace method and tree randomisation.