Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles

• URL: http://arxiv.org/abs/2605.12803v1
• Date: Tue, 12 May 2026 22:53:03 GMT
• Title: Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
• Abstract summary: disagreement-based uncertainty has shown promise in neural networks, but its adaptation to ensembles of incremental decision trees (IDTs) remains largely unexplored.<n>We investigate this approach by constructing batch-specific disagreement measures via label flipping in ensemble members.<n>Our experiments show that, although this method performs well in ensembles of multi-layer perceptrons (MLPs), it consistently underperforms loss-based detectors when applied to IDTs.
• Score: 1.2599533416395763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting concept drift in high-speed data streams remains challenging, particularly when models must operate on unlabeled data and avoid false alarms caused by benign shifts. While disagreement-based uncertainty has shown promise in neural networks, its adaptation to ensembles of incremental decision trees (IDTs) remains largely unexplored. We investigate this approach by constructing batch-specific disagreement measures via label flipping in ensemble members and evaluating their effectiveness for drift detection in tabular data streams. Our experiments show that, although this method performs well in ensembles of multi-layer perceptrons (MLPs), it consistently underperforms loss-based detectors when applied to IDTs. We attribute this behavior to the intrinsic rigidity of IDTs: learning primarily through structural expansion, with limited parameter adaptation, restricts model plasticity and prevents disagreement from reliably reflecting learning potential. Recent work on restructuring IDTs using their intrinsic decomposition into non-overlapping rules offers a promising direction for improving adaptability.

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