A Robust Bagging Method Using Median as a Combination
Rule
Authors
F. Zaman and H. Hirose
Source
IEEE 7th International Conference on Computer and Information
Technology 2008 (CIT2008), pp.55-60, July 8 - 11, 2008, University
of Technology, Sydney, Australia
Abstract
Bagging
has been known to be successful in increasing the accuracy of prediction
of the unstable classifiers. In bagging predictors are constructed
using bootstrap samples from the training sets and then aggregated
to form a bagged predictor. The robust bagging discard the bootstrapped
classifiers generating extreme error rates, as estimated by the
out-of-bag error rate and to combine over the remaining ones using
the robust location estimator,'median'. In this paper we try to
explore the advantages of robust bagging. We carried out experiments
on several benchmark data sets and suggest from the results that
robust bagging performs quite similar compare to the standard bagging
when applied to unstable base classifiers such as decision trees,
but performs better when applied to more stable base classifiers
as Fisher linear discriminant analysis and nearest mean classifier.