Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift

2026-06-20

AI Summary

This work addresses the limitation of existing static tabular datasets, which lack temporal structure and thus hinder the evaluation of model adaptability under controlled distribution shifts. To overcome this, the authors propose a clustering-based framework that transforms static data into controllable, evolving data streams through cluster-based partitioning and structured perturbations. Integrating the ADWIN drift detector with a sliding-window retraining mechanism, the framework systematically evaluates adaptation strategies across six model families, including tree ensembles and online learners. Experiments on five benchmark datasets for classification and regression demonstrate that the proposed methods—particularly Clustered Local ADWIN—accurately model and efficiently respond to localized drifts in feature space, significantly outperforming baseline approaches.

Abstract

Machine learning systems deployed in dynamic environments frequently operate under nonstationary data distributions, where controlled distribution shift can progressively degrade predictive performance. However, many widely used tabular benchmark datasets lack explicit temporal structure, limiting reproducible evaluation of drift adaptation methods. This work proposes a cluster-induced distribution shift simulation framework that transforms static tabular datasets into controlled evolving data streams through structured perturbations across featurespace partitions. Using this framework, six adaptation strategies are systematically evaluated: static learning, sliding-window retraining, global ADWIN retraining, cluster-local ADWIN retraining, random subspace drift detection, and feature-partitioned drift detection. Experiments are conducted on five benchmark datasets covering both classification and regression tasks using diverse predictive model families, including linear models, k-Nearest Neighbours, tree ensembles, boosting methods, and adaptive online learners.

Problem

• distribution shift
• concept drift
• model adaptation
• tabular data
• nonstationary data

Innovation

• cluster-induced distribution shift
• localized drift detection
• controlled distribution shift
• batch model adaptation
• structured perturbations