Scalable Ensemble Learning by Adaptive Sampling

Scalability has become an increasingly critical problem for successful data mining and knowledge discovery applications in real world where we often encounter extremely huge data sets that will render the traditional learning algorithms infeasible. Among various approaches to scalable learning, sampling techniques can be exploited to address the issue of scalability. This paper presents a brief outline on how to utilize the new sampling method in [3] to develop a scalable ensemble learning method with Boosting. Preliminary experimental results using benchmark data sets from the UC-Irvine ML data repository are also presented confirming the efficiency and competitive prediction accuracy of the proposed adaptive boosting method.