Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection

TL;DR

This paper introduces ICD3, a novel method for detecting concept drift in imbalanced streaming data by focusing on small and large clusters separately, improving interpretability and robustness.

Contribution

The paper proposes a new unbiased approach for detecting concept drift in imbalanced data using multi-distribution search and per-concept classifiers, addressing masking effects.

Findings

ICD3 outperforms state-of-the-art methods on benchmark datasets.

It effectively locates drifted concepts, including small clusters.

The approach is robust to varying imbalance ratios.

Abstract

Unlabeled streaming data are usually collected to describe dynamic systems, where concept drift detection is a vital prerequisite to understanding the evolution of systems. However, the drifting concepts are usually imbalanced in most real cases, which brings great challenges to drift detection. That is, the dominant statistics of large clusters can easily mask the drifting of small cluster distributions (also called small concepts), which is known as the `masking effect'. Considering that most existing approaches only detect the overall existence of drift under the assumption of balanced concepts, two critical problems arise: 1) where the small concept is, and 2) how to detect its drift. To address the challenging concept drift detection for imbalanced data, we propose Imbalanced Cluster Descriptor-based Drift Detection (ICD3) approach that is unbiased to the imbalanced concepts. This approach first detects imbalanced concepts by employing a newly designed multi-distribution-granular search, which ensures that the distribution of both small and large concepts is effectively captured. Subsequently, it trains a One-Cluster Classifier (OCC) for each identified concept to carefully monitor their potential drifts in the upcoming data chunks. Since the detection is independently performed for each concept, the dominance of large clusters is thus circumvented. ICD3 demonstrates highly interpretability by specifically locating the drifted concepts, and is robust to the changing of the imbalance ratio of concepts. Comprehensive experiments with multi-aspect ablation studies conducted on various benchmark datasets demonstrate the superiority of ICD3 against the state-of-the-art counterparts.