Bagging for linear classifiers
Classifiers built on small training sets are usually biased or unstable. Different techniques exist to construct more stable classifiers. It is not clear which ones are good, and whether they really stabilize the classifier or just improve the performance. In this paper bagging (bootstrapping and aggregating) [L. Breiman, Bagging predictors, Machine Learning J. 24(2), 123140 (1996)] is studied for a number of linear classifiers. A measure for the instability of classifiers is introduced. The influence of regularization and bagging on this instability and the generalization error of linear classifiers is investigated. In a simulation study it is shown that in general bagging is not a stabilizing technique. It is also demonstrated that one can consider the instability of the classifier to predict how useful bagging will be. Finally, it is shown experimentally that bagging might improve the performance of the classifier only for very unstable situations.
bagging instability regularization generalization error linear discriminant small sample size bias and variance