Learning Ensembles from Bites: A Scalable and Accurate Approach
Abstract
Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive data sets, because the size of the data set can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small votes") is a promising approach for learning from massive data sets, one that can utilize the power of boosting and bagging. We propose a framework for building hundreds or thousands of such classifiers on small subsets of data in a distributed environment. Experiments show this approach is fast, accurate, and scalable.
Cite
Text
Chawla et al. "Learning Ensembles from Bites: A Scalable and Accurate Approach." Journal of Machine Learning Research, 2004.Markdown
[Chawla et al. "Learning Ensembles from Bites: A Scalable and Accurate Approach." Journal of Machine Learning Research, 2004.](https://mlanthology.org/jmlr/2004/chawla2004jmlr-learning/)BibTeX
@article{chawla2004jmlr-learning,
title = {{Learning Ensembles from Bites: A Scalable and Accurate Approach}},
author = {Chawla, Nitesh V. and Hall, Lawrence O. and Bowyer, Kevin W. and Kegelmeyer, W. Philip},
journal = {Journal of Machine Learning Research},
year = {2004},
pages = {421-451},
volume = {5},
url = {https://mlanthology.org/jmlr/2004/chawla2004jmlr-learning/}
}