One of the potential advantages of multiple classifier sys- tems is an increased robustness to noise and other imperfections in data. Previous experiments on classification noise have shown that bagging is fairly robust but that boosting is quite sensitive. Decorate is a recently introduced ensemble method that constructs diverse committees using artificial data. It has been shown to generally outperform both boosting and bagging when training data is limited. This paper compares the sen- sitivity of bagging, boosting, and Decorate to three types of imperfect data: missing features, classification noise, and feature noise. For miss- ing data, Decorate is the most robust. For classification noise, bagging and Decorate are both robust, with bagging being slightly better than Decorate, while boosting is quite sensitive. For feature noise, all of the ensemble methods increase the resilience of the base classifier. In addition to their many other advantages, multiple-classifier systems hold the promise of developing learning methods that are robust in the presence of im- perfections in the data; in terms of missing features, and noise in both the class labels and the features. Noisy training data tends to increase the variance in the results produced by a given classifier; however, by learning a committee of hypotheses and combining their decisions, this variance can be reduced. In par- ticular, variance-reducing methods such as Bagging (2) have been shown to be robust in the presence of fairly high levels of noise, and can even benefit from low levels of noise (3). Bagging is a fairly simple ensemble method which is generally outperformed by more sophisticated techniques such as AdaBoost (4, 13). However, Ad- aBoost has a tendency to overfit when there is significant noise in the training data, preventing it from learning an eective ensemble (3). Therefore, there is a need for a general ensemble meta-learner 1 that is at least as accurate as Ad- aBoost when there is little or no noise, but is more robust to higher levels of
['Prem Melville', 'Nishit Shah', 'Lilyana Mihalkova', 'Raymond J. Mooney']
Experiments on Ensembles with Missing and Noisy Data