A scalable boosting learner for multi-class classification using adaptive sampling

Abstract

Scalability has become an increasingly critical issue for successful data mining applications in the "big data" era in which extremely huge data sets render traditional learning algorithms infeasible. Among various approaches to scalable learning, sampling techniques can be exploited to address the issue of scalability. This paper presents our study on applying a newly developed sampling-based boosting learning method for multi-class (non-binary) classification. Preliminary experimental results using bench-mark data sets from the UC-Irvine ML data repository confirm the efficiency and competitive prediction accuracy of the proposed adaptive boosting method for the multi-class classification task. We also show a formulation of using a single ensemble of non-binary base classifiers with adaptive sampling for multi-class problems. © 2014 Springer International Publishing.