Parallelized Boosting with Map-Reduce

In this paper, we propose two novel algorithms, ADABOOST.PL (Parallel ADABOOST) and LOGITBOOST.PL (Parallel LOGITBOOST), that facilitate simultaneous participation of multiple computing nodes to construct a boosted classifier. Our algorithms can induce boosted models whose generalization performance is close to the respective baseline classifier. By exploiting their own parallel architecture both the algorithms gain significant speedup. We used the Map-Reduce framework to implement our algorithms and experimented on a variety of synthetic and real-world data sets to demonstrate the performance in terms of classification accuracy, speedup and scaleup.