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Available works addressing multi-label classification in a data streamenvironment focus on proposing accurate models; however, these models oftenexhibit inefficiency and cannot balance effectiveness and efficiency. In thiswork, we propose a neural network-based approach that tackles this issue and issuitable for high-dimensional multi-label classification. Our model uses aselective concept drift adaptation mechanism that makes it suitable for anon-stationary environment. Additionally, we adapt our model to an environmentwith missing labels using a simple yet effective imputation strategy anddemonstrate that it outperforms a vast majority of the state-of-the-artsupervised models. To achieve our purposes, we introduce a weighted binaryrelevance-based approach named ML-BELS using the Broad Ensemble Learning System(BELS) as its base classifier. Instead of a chain of stacked classifiers, ourmodel employs independent weighted ensembles, with the weights generated by thepredictions of a BELS classifier. We show that using the weighting strategy ondatasets with low label cardinality negatively impacts the accuracy of themodel; with this in mind, we use the label cardinality as a trigger forapplying the weights. We present an extensive assessment of our model using 11state-of-the-art baselines, five synthetics, and 13 real-world datasets, allwith different characteristics. Our results demonstrate that the proposedapproach ML-BELS is successful in balancing effectiveness and efficiency, andis robust to missing labels and concept drift.

Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification