Machine Learning
The Journal of Machine Learning Research
Stability of Randomized Learning Algorithms
The Journal of Machine Learning Research
A comparative study on the performance of several ensemble methods with low subsampling ratio
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Estimation of optimal sample size of decision forest with SVM using embedded cross-validation method
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
On selecting additional predictive models in double bagging type ensemble method
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
Artificial Intelligence Review
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In ensemble methods to create multiple classifiers mostly bootstrap sampling method is preferred. The use of subsampling in ensemble creation, produce diverse members for the ensemble and induce instability for stable classifiers. In subsampling the only parameter is the subsample rate that is how much observations we will take from the training sample in each subsample. In this paper we have presented our work on the effect of different subsampling rate (SSR) in bagging type ensemble of stable classifiers, Subbagging and Double Subbagging. We have used three stable classifiers, Linear Support Vector Machine (LSVM), Stable Linear Discriminant Analysis (SLDA) and Logistic Linear Classifier (LOGLC). We also experimented on decision tree to check whether the performance of tree classifier is influenced by different SSR. From the experiment we see that for most of the datasets, the subbagging with stable classifiers in low SSR produces better performance than bagging and single stable classifiers, also in some cases better than double subbagging. We also found an opposite relation between the performance of double subbagging and subbagging.