Multi-label data streams is a highly challenging task involving drifts in features and labels. Classifiers must automatically adapt to changes while keeping a competitive accuracy in a real-time dynamic environment where the frequencies of the labelsets are non-stationary and highly imbalanced. This paper presents a multi-label k Nearest Neighbor (kNN) with Self Adjusting Memory (SAM) for drifting data streams (ML-SAM-kNN). It exploits short- and long-term memories to predict the current and evolving states of the data stream. The experimental study compares the proposal with eight other multi-label classifiers for data streams on 23 datasets on six multi-label metrics, evaluation time, and memory consumption. Non-parametric statistical analysis of the results shows the superiority of ML-SAM-kNN, including when compared with ML-kNN.
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