Name
Abstract | ||
|---|---|---|
Bagging and boosting are well-known ensemble learning methods. They combine multiple learned base models with the aim of improving generalization performance. To date, they have been used primarily in batch mode, i.e., they require multiple passes through the training data. In previous work, we presented online bagging and boosting algorithms that only require one pass through the training data and presented experimental results on some relatively small datasets. Through additional experiments on a variety of larger synthetic and real datasets, this paper demonstrates that our online versions perform comparably to their batch counterparts in terms of classification accuracy. We also demonstrate the substantial reduction in running time we obtain with our online algorithms because they require fewer passes through the training data. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1145/502512.502565 | KDD |
Keywords | Field | DocType |
training data,online bagging,online algorithm,multiple pass,additional experiment,batch mode,batch counterpart,experimental comparison,online version,real datasets,batch version,small datasets,ensemble learning,mcmc | Training set,Data mining,Online algorithm,Markov chain Monte Carlo,Computer science,Artificial intelligence,Boosting (machine learning),Event sequence,Batch processing,Ensemble learning,Machine learning | Conference |
ISBN | Citations | PageRank |
1-58113-391-X | 99 | 9.10 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| nikunj c oza | 1 | 694 | 54.32 |
| Stuart J. Russell | 2 | 5731 | 796.47 |