Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Effect of Subsampling Rate on Subbagging and Related Ensembles of Stable Classifiers
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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In this paper the performance of the m-out-of-n decision forest of SVM without replacement with different subsampling ratio (m/n) is analyzed in terms of an embedded cross-validation technique. The subsampling ratio plays a pivotal role in improving the performance of the decision forest of SVM. Because the SVM in this ensemble enlarge the feature space of the underlying base decision tree classifiers and guarantees a improved performance of the ensemble overall. To ensure the better training of the SVM generally the out-of-bag sample is kept larger but there is no general rule to estimate the optimal sample size for the decision forest. In this paper we propose to use the embedded crossvalidation method to select the a near optimum value of the sampling ratio. In our criterion the decision forest of SVM trained on independent samples whose size is such that the cross-validation error of that ensemble is as low as possible, will produce an improved generalization performance for the ensemble.