Distributed Pasting of Small Votes

Bagging and boosting are two popular ensemble methods that 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. Pasting small votes can utilize the power of boosting and bagging, and potentially scale up to massive datasets. 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 to massive datasets.

• Type: Journal
• Year: 2002
• Where: MCS
• Authors: Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer
• Keywords: Massive Datasets, MCS 2002, Pattern Recognition, Popular Ensemble Methods, Small Votes