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IMI/Publicaţii/CSJM/Ediţii/CSJM v.33, n.2 (98), 2025/

Enhanced Green Accelerated Hoeffding Trees for Improved Data Stream Classification

Authors: Hadjer Imene Bensaoula, Sarah Nait Bahloul
Keywords: Big Data, Data Stream, Online Learning, Incremental Classification, Hoeffding Trees, Hoeffding Bound.

Abstract

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.

Hadjer Imene Bensaoula
ORCID: https://orcid.org/0009-0000-0592-733X
SIMPA Laboratory, Computer Science Department, University of Science and
Technology of Oran Mohamed Boudiaf USTO-MB, Oran, Algeria
E-mail:

Sarah Nait Bahloul
ORCID: https://orcid.org/0000-0001-9219-8381
LSSD Laboratory, Computer Science Department, University of Science and Technology
of Oran Mohamed Boudiaf USTO-MB, Oran, Algeria
E-mail:

DOI

https://doi.org/10.56415/csjm.v33.09

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