# Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model

arXiv: https://arxiv.org/abs/2102.05983 · HTML: https://mlsanity.com/paper/2102.05983/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams](https://mlsanity.com/paper/1709.02457/) (2017)
- [An Ensemble Generation Method Based on Instance Hardness](https://mlsanity.com/paper/1804.07419/) (2018)

## Built on by (most cited first, 9 of 9)

- [Online Boosting Adaptive Learning under Concept Drift for Multistream Classification](https://mlsanity.com/paper/2312.10841/) (2023)
- [Active Weighted Aging Ensemble for Drifted Data Stream Classification](https://mlsanity.com/paper/2112.10150/) (2021)
- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [Tracking changes using Kullback-Leibler divergence for the continual learning](https://mlsanity.com/paper/2210.04865/) (2022)
- [Passive learning to address nonstationarity in virtual flow metering applications](https://mlsanity.com/paper/2202.03236/) (2022)
- [Variations on the Expectation due to Changes in the Probability Measure](https://mlsanity.com/paper/2502.02887/) (2025)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [Thwarting Cybersecurity Attacks with Explainable Concept Drift](https://mlsanity.com/paper/2403.13023/) (2024)
- [FMMO: Detecting the Divergence Between Local Attribution and Global Drift](https://mlsanity.com/paper/2609.06173/) (2026)

## Abstract (arXiv)

Real-world applications have been dealing with large amounts of data that arrive over time and generally present changes in their underlying joint probability distribution, i.e., concept drift. Concept drift can be subdivided into two types: virtual drift, which affects the unconditional probability distribution p(x), and real drift, which affects the conditional probability distribution p(y|x). Existing works focuses on real drift. However, strategies to cope with real drift may not be the best suited for dealing with virtual drift, since the real class boundaries remain unchanged. We provide the first in depth analysis of the differences between the impact of virtual and real drifts on classifiers' suitability. We propose an approach to handle both drifts called On-line Gaussian Mixture Model With Noise Filter For Handling Virtual and Real Concept Drifts (OGMMF-VRD). Experiments with 7 synthetic and 3 real-world datasets show that OGMMF-VRD obtained the best results in terms of average accuracy, G-mean and runtime compared to existing approaches. Moreover, its accuracy over time suffered less performance degradation in the presence of drifts.