Bagging, Boosting and the Random Subspace Method for Linear Classifiers
Purchase on Springer.com
$39.95 / €34.95 / £29.95*
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.
Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented.
- Bagging, Boosting and the Random Subspace Method for Linear Classifiers
Pattern Analysis & Applications
Volume 5, Issue 2 , pp 121-135
- Cover Date
- Print ISSN
- Springer-Verlag London Limited
- Additional Links
- Key words: Bagging; Boosting; Combining classifiers; Linear classifiers; Random subspaces; Training sample size
- Industry Sectors