Unsupervised Drift Detection Using Quadtree Spatial Mapping
Bernardo A. Ramos, Cristiano Leite de Castro, Tiago A. Coelho, Plamen Parvanov Angelov · 2024
This paper presents an unsupervised and model-independent concept drift detector based on quadtree spatial analysis (QTS).We used a d-dimensional quadtree to map the feature space and tracked a univariate curve that mimics the spatial behavior of the data stream.This curve serves as a helpful visual tool for analyzing concept drifts.Drifts are identified when there is a significant change in the current spatial mapping.Experimental results show that the proposed outperformed well-known drift detectors in terms of average precision and F1-score.