Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

2026-08-17

AI Summary

This study addresses the insufficient diversity of adaptive splitting trees when employed as base learners in ensembles for data stream classification. We propose the Hoeffding Adaptive Splitting Tree, which integrates periodic splitting strategies with adaptive mechanisms. By leveraging change detection to precisely identify performance degradation and determine optimal split points, this approach effectively enhances ensemble diversity. Experimental results demonstrate that the proposed model achieves an optimal trade-off between classification accuracy and computational efficiency under concept drift scenarios. Furthermore, it attains state-of-the-art performance across benchmark evaluations, computational cost analyses, and drift adaptability assessments, thereby providing an efficient solution for learning from streaming data.

Abstract

Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as base learners, performing periodic split attempts according to the Hoeffding bound. Recent studies, however, indicate that this standard splitting mechanism lacks adaptability, while adaptive trees that trigger splits in response to performance degradation have achieved superior results. In this paper, we identify limitations in the use of adaptive-splitting decision trees as ensemble base learners, showing that change detectors often fail to promote sufficient diversity within ensembles. To address this issue, we propose two novel decision tree models, termed Hoeffding Adaptive Splitting Trees. These models combine the periodic splitting strategy of Hoeffding Trees, which fosters ensemble diversity, with adaptive splitting mechanisms that employ change detection algorithms to identify performance decay and determine split points. Experimental results demonstrate that Hoeffding Adaptive Splitting Trees enhance ensemble performance and achieve state-of-the-art results across a comprehensive evaluation, including benchmark comparisons, computational cost analysis, and concept drift adaptation.

Problem

• Data stream classification
• Concept drift
• Ensemble learning
• Adaptive splitting trees
• Ensemble diversity

Innovation

• Hoeffding Adaptive Splitting Trees
• Data Stream Classification
• Concept Drift
• Ensemble Learning
• Adaptive Splitting