Effect of Subsampling Rate on Subbagging and Related Ensembles of Stable Classifiers


Faisal Zaman, Hideo Hirose


PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence Pages 44-49
oŕ: Springer-Verlag Berlin, Heidelberg 2009
table of contents ISBN: 978-3-642-11163-1 doi>10.1007/978-3-642-11164-8_8

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.

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