Enhanced Green Accelerated Hoeffding Trees for Improved Data Stream Classification

The proliferation of real-time, infinite data streams necessitates efficient online learning approaches. Hoeffding Trees (HT), which extend traditional decision trees using the Hoeffding bound, offer robust stream classification but face high computational costs. While the Green Accelerated Hoeffding Tree (GAHT) addresses energy efficiency concerns, its prediction accuracy can be improved by addressing its inherent limitations in combining Hoeffding bounds with information gain metrics for incrementally growing the tree. This study successfully develops enhanced GAHT variants through optimized Hoeffding bound stability and node splitting mechanisms. Our empirical evaluation demonstrates that the usage of these new variants improves predictive performance over the state-of-the-art GAHT, without compromising its energy efficiency.