Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Computational Statistics & Data Analysis - Nonlinear methods and data mining
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
An efficient modified boosting method for solving classification problems
Journal of Computational and Applied Mathematics
Bundling classifiers by bagging trees
Computational Statistics & Data Analysis
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
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
| Hi-index | 0.00 |
In ensemble methods each base learner is trained on a resampled version of the original training sample with the same size. In this paper we have used resampling without replacement or subsampling to train base classifiers with low subsample ratio i.e., the size of each subsample is smaller than the original training sample. The main objective of this paper is to check if the scalability performance of several well known ensemble methods with low subsample ratio are competent and compare them with their original counterpart. We have selected three ensemble methods: Bagging, Adaboost and Bundling. In all the ensemble methods a full decision tree is used as the base classifier. We have applied the subsampled version of the above ensembles in several well known benchmark datasets to check the error rate. We have also checked the time complexity of each ensemble method with low subsampling ratio. From the experiments, it is apparent that in the case of bagging and adaboost with low subsampling ratio for most of the cases the error rate is inversely related with subsample size, while for bundling it is opposite. Overall performance of the ensemble methods with low subsampling ratio from experiments showed that bundling is superior in accuracy with low subsampling ratio in almost all the datasets, while bagging is superior in reducing time complexity.