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

TL;DR

This paper introduces OGMMF-VRD, an adaptive Gaussian mixture model designed to effectively handle both virtual and real concept drifts in streaming data, outperforming existing methods in accuracy and robustness.

Contribution

It provides the first comprehensive analysis of virtual versus real concept drifts and proposes a novel model that adapts to both types simultaneously.

Findings

OGMMF-VRD achieved the highest accuracy and G-mean scores.

The approach demonstrated less performance degradation over time.

It outperformed existing methods in runtime efficiency.

Abstract

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.