  Many real-world applications adopt multi-label data streams as the need for
algorithms to deal with rapidly changing data increases. Changes in data
distribution, also known as concept drift, cause the existing classification
models to rapidly lose their effectiveness. To assist the classifiers, we
propose a novel algorithm called Label Dependency Drift Detector (LD3), an
implicit (unsupervised) concept drift detector using label dependencies within
the data for multi-label data streams. Our study exploits the dynamic temporal
dependencies between labels using a label influence ranking method, which
leverages a data fusion algorithm and uses the produced ranking to detect
concept drift. LD3 is the first unsupervised concept drift detection algorithm
in the multi-label classification problem area. In this study, we perform an
extensive evaluation of LD3 by comparing it with 14 prevalent supervised
concept drift detection algorithms that we adapt to the problem area using 12
datasets and a baseline classifier. The results show that LD3 provides between
19.8\% and 68.6\% better predictive performance than comparable detectors on
both real-world and synthetic data streams.
