Adaptive random tree ensemble for evolving data stream classification

doi:10.1016/j.knosys.2024.112830

Adaptive random tree ensemble for evolving data stream classification

2025-01-01

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DOI：10.1016/j.knosys.2024.112830

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Data stream mining with concept drift is a significant challenge in machine learning because this scenario requires the ability to handle unlimited and ever-changing data and real-time processing. An often employed strategy in data stream mining involves utilizing ensembles due to their capability to tackle concept drift and attain remarkably accurate predictions. However, developing a precise and efficient ensemble for data stream mining poses a significant challenge, as state-of-the-art algorithms are often highly inefficient, consuming excessive memory and processing time. In this study, we propose a novel ensemble-based classification algorithm for data streams named Adaptive Random Tree Ensemble (ARTE). The algorithm explores approaches that promote high prediction accuracy using a random-sized feature subspace for each element of the ensemble, online bagging, random choice of the cut-point for splitting the trees, and a method of classifier selection for final ensemble voting. This study also presents analyses on the contribution of the choice of subspace size and the random cut-point for splitting the tree's nodes to the ensemble's diversity. Following an extensive experimental investigation, ARTE exhibited high predictive performance and outperformed state-of-the-art ensembles on data streams for real and synthetic datasets while requiring fewer computational resources.

Keywords:

Data stream mining

Ensemble learning

Concept drift

Random subspaces

Adaptive random tree ensemble for evolving data stream classification

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