@ARTICLE{Chawla_04,
    AUTHOR      = {Nitesh V. Chawla and Lawrence O. Hall and Kevin W. Bowyer and W. Philip Kegelmeyer},
    TITLE       = {{Learning Ensembles from Bites: A Scalable and Accurate Approach}},
    JOURNAL     = {{Journal of Machine Learning Research}},
    YEAR        = {2004},
    VOLUME      = {5},
    NUMBER      = {},
    PAGES       = {421--451},
    MONTH       = {April},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://delivery.acm.org/10.1145/1010000/1005347/p421-chawla.pdf?key1=1005347&key2=3527580901&coll=GUIDE&dl=GUIDE&CFID=24745947&CFTOKEN=29878325},
    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.},
}