Intelligent MapReduce Based Framework for Labeling Instances in Evolving Data Stream

摘要

In our current work, we have proposed a multi-tiered ensemble based robust method to address all of the challenges of labeling instances in evolving data stream. Bottleneck of our current work is, it needs to build ADABOOST ensembles for each of the numeric features. This can face scalability issue as number of features can be very large at times in data stream. In this paper, we propose an intelligent approach to build these large number of ADABOOST ensembles with MapReduce based parallelism. We show that, this approach can help our base method to achieve significant scalability without compromising classification accuracy. We analyze different aspects of our design to depict advantages and disadvantages of the approach. We also compare and analyze performance of the proposed approach in terms of execution time, speedup and scale up.