Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection

TL;DR

This work proposes Imbalanced Cluster Descriptor-based Drift Detection (ICD3) approach, which 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.

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.

Figures (8)

• Figure 1: Comparison of drift detection in balanced and imbalanced cases. The chunk-wise drift detection can easily become incompetent under the imbalanced case because even though a significant drift appears on a small cluster, the corresponding impact can almost be masked by the large clusters in terms of the overall density distribution.
• Figure 2: Overall workflow of ICD3. ICD3 works on two adjacent chunks. In the base chunk $D^t$, fine-grained prototypes are first learned and used to partition the samples into multiple sub-clusters. Next, fusion queues are learned to merge these sub-clusters into interpretable clusters. Following this, descriptors are trained for each of the clusters. The learned prototypes guide the capture of the distribution in the incoming chunk $D^{t+1}$, while the fusion queues guide the merging of sub-clusters into clusters. Concept drift detection is performed through the descriptors inherited from the base chunk.
• Figure 3: Comparison of: 1) one OCC for one chunk, 2) multi-class classifiers, and 3) one OCC for one cluster, in the scenario of concept drift detection.
• Figure 4: Accuracy across chunks on 2D-4G-C and Noaa dataset.
• Figure 5: Comparison of the Accuracy of ICD3 and counterparts across different imbalance ratios.

Theorems & Definitions (6)

• Definition 1: Reverse Nearest Neighbors
• Remark 1: Advantages of multiple OCCs in concept drift detection.
• Remark 2: Unbiased sample partition in $D^m$
• Remark 3: Concept drift understanding
• Theorem 1
• proof