# Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection

arXiv: https://arxiv.org/abs/2603.06757 · HTML: https://mlsanity.com/paper/2603.06757/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [Unsupervised Continual Learning in Streaming Environments](https://mlsanity.com/paper/2109.09282/) (2021)
- [Concept Drift Detection from Multi-Class Imbalanced Data Streams](https://mlsanity.com/paper/2104.10228/) (2021)
- [Online Drift Detection with Maximum Concept Discrepancy](https://mlsanity.com/paper/2407.05375/) (2024)
- [Adaptive Online Incremental Learning for Evolving Data Streams](https://mlsanity.com/paper/2201.01633/) (2022)
- [Asynchronous Federated Clustering with Unknown Number of Clusters](https://mlsanity.com/paper/2412.20341/) (2024)
- [Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering](https://mlsanity.com/paper/2404.09243/) (2024)
- [Concept Drift Detection via Equal Intensity k-means Space Partitioning](https://mlsanity.com/paper/2004.11587/) (2020)
- [Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning](https://mlsanity.com/paper/2601.16491/) (2026)
- [Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree](https://mlsanity.com/paper/2208.14801/) (2022)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers](https://mlsanity.com/paper/2603.12847/) (2026)
- [Autonomous Drift Learning in Data Streams: A Unified Perspective](https://mlsanity.com/paper/2605.01295/) (2026)

## Abstract (arXiv)

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.