Adaptive Communication for Scalable Distributed AdaBoost Framework

Abstract

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In the current reality of big data in area of information industry, issues related to distributed and parallel algorithms for data analysis are relevant. In particular, big data appear at the training stage of machine learning algorithms. Boosting algorithms are a vivid representative of this group of algorithms. When data is large, distributed and parallel processing by boosting type algorithms is possible, but it is associated with sharp increase pf processing time. On the one hand, time increases due to the requirement to ensure the necessary accuracy of problem solutions, on the other, due to the need to transport distributed data and synchronize the entire computation process. Discussions and experiments conducted on this topic are provided.