Learning from Data Stream Based on Random Projection and Hoeffding Tree Classifier

Xuan Cuong Pham, Manh Truong Dang, Dinh Viet Sang, Son Hoang, Tien Thanh Nguyen, Alan Wee-Chung Liew

Abstract: In this study, we introduce an ensemble-based approach for online machine learning. Here, instead of working on the original data, several Hoeffding tree classifiers classify and are updated on the lower dimensional projected data generated from originality by random projections. Since random projection is unstable, from one example, many diverse training data can be created to train the set of Hoeffding tree classifiers. The experiments conducted on a number of datasets chosen from different sources demonstrate that the proposed approach performs significantly better than the single Hoeffding tree and some well-known online learning algorithms including additive models and Online Bagging.