Learning Ensembles from Bites: A Scalable and Accurate Approach
Nitesh V. Chawla, Lawrence Hall, Kevin W. Bowyer, W. Philip Kegelmeyer · 2004
Bagging and boosting are two popular ensemble methods that typically achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data ("pasting small votes") is a promising approach for learning from massive datasets, 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.