Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels

TL;DR

This paper introduces a hierarchical hypothesis testing framework with a request-and-reverify strategy for detecting concept drift in streaming data, requiring fewer labels and outperforming existing methods.

Contribution

The paper proposes a novel framework and two methods for concept drift detection that reduce label dependency and improve detection accuracy in streaming data.

Findings

Outperforms state-of-the-art unsupervised drift detectors.

Achieves better performance than DDM with fewer labels.

Demonstrates effectiveness on benchmark datasets.

Abstract

One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adapt the model so as to mitigate any deterioration in the model's predictive performance. Unfortunately, most existing concept drift detection methods rely on a strong and over-optimistic condition that the true labels are available immediately for all already classified instances. In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary. Two methods, namely Hierarchical Hypothesis Testing with Classification Uncertainty (HHT-CU) and Hierarchical Hypothesis Testing with Attribute-wise "Goodness-of-fit" (HHT-AG), are proposed respectively under the novel framework. In experiments with benchmark datasets, our methods demonstrate overwhelming advantages over state-of-the-art unsupervised drift detectors. More importantly, our methods even outperform DDM (the widely used supervised drift detector) when we use significantly fewer labels.