Boosting with Adaptive Sampling for Multi-class Classification

Achieving scalability of learning algorithms has become an increasingly critical issue in knowledge discovery from "big data". Sampling techniques can be exploited as one of the approaches to address the issue of scalability. We present in this paper a method to employ a newly developed sampling-based ensemble learning method by boosting for multi-class (non-binary) classification. This current research extends our previous work on multi-class classification with sampling-based ensemble learning method, in which the base classifiers are the most simplistic, such as decision stumps. Here we generalize the sampling-based method to handle more complex base classifiers such as decision trees in building an ensemble, which require sampling a set of instances before building a base classifier. We present experimental results using bench-mark data sets from the UC-Irvine ML data repository that confirm the efficiency and competitive prediction accuracy of the proposed adaptive boosting method for the multi-class classification task.