Parallelizing AdaBoost by weights dynamics

Authors:
Stefano Merler;Bruno Caprile;Cesare Furlanello
Affiliations:
ITC-irst - Centro per la Ricerca Scientifica e Tecnologica, via Sommarive 18, I-38050 Povo (Trento), Italy;ITC-irst - Centro per la Ricerca Scientifica e Tecnologica, via Sommarive 18, I-38050 Povo (Trento), Italy;ITC-irst - Centro per la Ricerca Scientifica e Tecnologica, via Sommarive 18, I-38050 Povo (Trento), Italy
Venue:
Computational Statistics & Data Analysis
Year:
2007

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Abstract

AdaBoost is one of the most popular classification methods. In contrast to other ensemble methods (e.g., Bagging) the AdaBoost is inherently sequential. In many data intensive real-world situations this may limit the practical applicability of the method. P-AdaBoost is a novel scheme for the parallelization of AdaBoost, which builds upon earlier results concerning the dynamics of AdaBoost weights. P-AdaBoost yields approximations to the standard AdaBoost models that can be easily and efficiently distributed over a network of computing nodes. Properties of P-AdaBoost as a stochastic minimizer of the AdaBoost cost functional are discussed. Experiments are reported on both synthetic and benchmark data sets.