Learning Ensembles from Bites: A Scalable and Accurate Approach

Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer; 5(Apr):421--451, 2004.

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.

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