Sampling Adaptively Using the Massart Inequality for Scalable Learning

With the advent of the "big data" era, the data mining community is facing an increasingly critical problem of developing scalable algorithms capable of mining knowledge from massive amount of data. This paper develops a sampling-based method to address the issue of scalability. We show how to utilize the new, adaptive sampling method in [4] to develop a scalable learning algorithm by boosting, an ensemble learning method. We present experimental results using bench-mark data sets from the UC-Irvine ML data repository that confirm the much improved efficiency and thus scalability, and competitive prediction accuracy of the new adaptive boosting method, in comparison with existing approaches.