A New Framework for Distributed Boosting Algorithm
Nguyen Thi Van Uyen, Tae Choong Chung · 2007
In this paper, we propose a new framework for building boosting classifier on distributed databases. The main idea of our method is to utilize the parallelism of distributed databases. At each round of the algorithm, each site processes its own data locally, and calculates all needed information. A center site will collect information from all sites and build the global classifier, which is then a classifier in the ensemble. This global classifier is also used by each distributed site to compute required information for the next round. By repeating this process, we will have an ensemble of classifier from distributed database that is almost identical to the one built on the whole data. The experiment results show that the accuracy of our proposed method is almost equal to the accuracy when applying boosting algorithm to the whole dataset.