Lecture Notes in Computer Science vol:4212 pages:809-816
European Conference on Machine Learning location:Berlin, Germany date:September 18-22, 2006
Bagging is an ensemble method that relies on random resampling of a data set to construct models for the ensemble. When only statistics about the data are available, but no individual examples, the straightforward resampling procedure cannot be implemented. The question is then whether bagging can somehow be simulated. In this paper we propose a method that, instead of computing certain heuristics (such as information gain) from a resampled version of the data, estimates the probability distribution of these heuristics under random resampling, and then samples from this distribution. The resulting method is not entirely equivalent to bagging because it ignores certain dependencies among statistics. Nevertheless, experiments show that this “simulated bagging” yields similar accuracy as bagging, while being as efficient and more generally applicable.